| Intuitive Sensemaking: From Theory to Simulation Based Training | | BIBAK | Full-Text | 3-10 | |
| Kathleen Bartlett; Margaret Nolan; Andrea Marraffino | |||
| The concept of sensemaking has become a prominent component of military
operations in ambiguous environments. Sensemaking, in general, describes the
process of pattern recognition, semantic formulation, anticipation, and
holistic understanding and supports sociocultural situation assessment, anomaly
detection, and anticipatory thinking. This skill enables intuitive experts to
rapidly draw accurate conclusions based on cues that others cannot discern or
to attend to the most important cues, based on experience. Simulation-based
training can enhance and accelerate the ability to recognize and analyze cues
and patterns by translating the unconscious, automatic monitoring and
integration practiced by experts into a conscious cognitive process that we
call intuitive sensemaking. We describe an Office of Naval Research project,
currently in development, intended to effectively train previously ambiguous
advanced cognitive skills such as intuition-informed sensemaking. With
training, teams of military personnel should see increases in cohesiveness,
sociocultural situation assessment, anomaly detection, and anticipatory
thinking. Keywords: Sensemaking; Intuition; Simulation-Based Training; Human-Computer
Interaction (HCI); Expertise; Implicit Learning | |||
| Using Simulation Based Training Methods for Improved Warfighter Decision Making | | BIBAK | Full-Text | 11-20 | |
| Perakath Benjamin; Paul Koola; Kumar Akella; Michael Graul; Michael Painter | |||
| Few efforts have greater significance to our warfighting capability than
those aimed at dramatically improving the skills, knowledge, and experience of
military decision makers. The research and technology ideas presented in this
paper are motivated by need to improve the quality of decision makers through
the design of innovative training technology for military decision makers. This
paper describes an adaptive simulation based training approach to improve the
effectiveness of warfighter decision making. The paper describes (i) a method
for adaptive simulation based training; (ii) a mission-driven approach to
measure trainee performance based on carefully designed metrics; and (iii) an
automation support architecture for adaptive simulation based training.
Examples are provided throughout the paper to illustrate key research ideas. Keywords: Simulation Based Training; Warfighter Decision Making; Adaptive Training;
Mission Driven Performance Measurement | |||
| Enhancing HMD-Based F-35 Training through Integration of Eye Tracking and Electroencephalography Technology | | BIBAK | Full-Text | 21-30 | |
| Meredith Carroll; Glenn Surpris; Shayna Strally; Matthew Archer; Frank Hannigan; Kelly Hale; Wink Bennett | |||
| The ever increasing complexity of knowledge, skills and abilities (KSAs)
demanded of Department of Defense (DoD) personnel has created the need to
develop tools to increase the efficiency and effectiveness of training. This is
especially true for the F-35, the first 5th-generation aircraft to use an HMD
as the primary instrument display. Additionally, the F-35 can perform
operations previously performed by multiple operators, which potentially places
incredible strain on the pilot's cognitive resources by exposing him to large
amounts of data from disparate sources. It is critical to ensure training
results in pilots learning optimal strategies for operating in this information
rich environment. This paper discusses current efforts to develop and evaluate
a performance monitoring and assessment system which integrates eye tracking
and Electroencephalography (EEG) technology into an HMD enabled F-35 training
environment to extend traditional behavioral metrics and better understand how
a pilot interacts with data presented in the HMD. Keywords: Training; Performance Assessment; Eye tracking; EEG; Helmet-mounted display;
Heads-up display; F-35 | |||
| Bio-reckoning: Perceptual User Interface Design for Military Training | | BIBAK | Full-Text | 31-40 | |
| Tami Griffith; Deanna Rumble; Pankaj Mahajan; Cali M. Fidopiastis | |||
| Simulation based training is one way to attain operational realism for
training complex military tasks in a safe, task relevant manner. For successful
transfer of knowledge, skills, and abilities to the dynamically changing
military environment, the human-computer interface should minimally support
learning during the training process and provide congruent action plans that
facilitate understanding of the overall training goal. While there are emerging
controller technologies, simulators still rely on such input devices as mouse
and keyboard. These devices potentially cause information and training
bottlenecks as they limit naturalistic interactivity within the more advanced
serious gaming platforms. Given the shortcomings of current interface design,
we suggest a human-computer interface framework that includes perceptual user
interface components and an open source serious game testbed. We discuss a
multimodal framework called bio-reckoning that integrates brain-computer
interface techniques, eye tracking, and facial recognition within EDGE, the
U.S. Army's newest serious game based training tool. Keywords: simulation based training; perceptual user interfaces; brain-computer
interfaces; serious games; military training; augmented cognition | |||
| Taiwanese EFLs' Metacognitive Awareness of Reading Strategy and Reading Comprehension | | BIBAK | Full-Text | 41-49 | |
| Yen-ju Hou | |||
| The study aims to identify the types of metacognitive awareness of reading
strategies that Taiwanese EFLs (English as Foreign Language) used at medical
junior colleges. In addition, metacognitive awareness of reading strategies
were investigated to discover whether or not it affects students' English
reading performance, specifically in reading comprehension. A total of 454
junior college students participated in the study. The results indicated that
problem-solving reading strategies were used the most, followed by globe
reading strategies, whereas support reading strategies were used the least.
Regarding of the effects of variables on English reading performance, overall
reading strategy use, and problem-solving reading strategies each significantly
predicted students' reading comprehension. It's hoped that the finding could be
helpful for further study as well as teaching. Keywords: Metacognitive awareness; reading strategy; reading comprehension | |||
| Automated Camera Selection and Control for Better Training Support | | BIBA | Full-Text | 50-59 | |
| Adrian Ilie; Greg Welch | |||
| Physical training ranges have been shown to be critical in helping trainees
integrate previously-perfected skills. There is a growing need for streamlining
the feedback participants receive after training. This need is being met by two
related research efforts: approaches for automated camera selection and
control, and computer vision-based approaches for automated extraction of
relevant training feedback information.
We introduce a framework for augmenting the capabilities present in training ranges that aims to help in both domains. Its main component is ASCENT (Automated Selection and Control for ENhanced Training), an automated camera selection and control approach for operators that also helps provide better training feedback to trainees. We have tested our camera control approach in simulated and laboratory settings, and are pursuing opportunities to deploy it at training ranges. In this paper we outline the elements of our framework and discuss its application for better training support. | |||
| A Hierarchical Behavior Analysis Approach for Automated Trainee Performance Evaluation in Training Ranges | | BIBA | Full-Text | 60-69 | |
| Saad Khan; Hui Cheng; Rakesh Kumar | |||
| In this paper we present a closed loop mixed reality training system that provides automatic assessment of trainee performance during kinetic military exercises. At the core of our system is a hierarchical behavior analysis approach that integrates a number of data sensor modalities including Audio/Video, RFID and IMUs to automatically capture trainee actions in a comprehensive manner. Our behavior analysis and performance evaluation framework uses a finite state machine (FSM) model in which trainee behaviors are the states of the training scenario and the transitions of states are caused by stimuli that we refer to as trigger events. The goal of behavior analysis is to estimate the states of the trainees with respect to the training scenario and quantify trainee performance. To robustly detect each state, we build classifiers for each behavioral state and trigger event. At a given time, based on the state estimation, a set of related classifiers are activated for detecting trigger events and states that can be transitioned to and from the current states. The overall structure of the FSM and trigger events is determined by a Training Ontology that is specific to the training scenario. | |||
| Augmenting Instructional Design with State-Based Assessment | | BIBAK | Full-Text | 70-79 | |
| Kevin Oden | |||
| The Trainee Engagement Management System (TEMS) is a technology-enabled
instructional design concept that leverages state-based assessment techniques
to improve training processes and outcomes. Specifically, the concept is
designed to support military instructors in the delivery of
empirically-supported instructional prompts to foster trainee engagement within
a Computer Based Training (CBT) environment. The central theme of the concept
is to augment, not replace, an instructor's abilities. By reducing workload
demands on an instructor, the approach enables the delivery of personalized
instruction in a one (instructor) to many (trainees) context. The TEMS concept
embraces a human-system philosophy and is designed to mitigate risks typically
associated with the transition of advanced technologies and concepts to field
settings. In this paper we discuss those challenges and describe the basic TEMS
architecture. Keywords: Instructional System Design; Augmented Cognition; Human Systems; Computer
Based Training | |||
| Instrumenting Competition-Based Exercises to Evaluate Cyber Defender Situation Awareness | | BIBA | Full-Text | 80-89 | |
| Theodore Reed; Kevin Nauer; Austin Silva | |||
| Cyber defense exercises create simulated attack and defense scenarios used to train and evaluate incident responders. The most pervasive form of competition-based exercise is comprised of jeopardy-style challenges, which compliment a fictional cyber-security event. Multiple competitions were instrumented to collect usage statistics on a per-challenge basis. The competitions use researcher-developed challenges containing over twenty attack techniques, which generate forensic evidence and observable second-order effects. The following observations were made: (1) a group of defenders performs better than an individual; (2) situation awareness of the fictional event may be measured; (3) challenge complexity does not imply difficulty. This research introduces a novel application of system instrumentation on competition-based exercises and describes an exercise development methodology for effective challenge and competition creation. Effective challenges correctly represent difficulty and reward competitors with objective points and optional forensic clues. Effective competitions compliment training goals and appropriately improve the knowledge and skill of a competitor. | |||
| Enhanced Training for Cyber Situational Awareness | | BIBAK | Full-Text | 90-99 | |
| Susan Stevens-Adams; Armida Carbajal; Austin Silva; Kevin Nauer; Benjamin Anderson; Theodore Reed; Chris Forsythe | |||
| A study was conducted in which participants received either tool-based or
narrative-based training and then completed challenges associated with network
security threats. Three teams were formed: (1) Tool-Based, for which each
participant received tool-based training; (2) Narrative-Based, for which each
participant received narrative-based training and (3) Combined, for which three
participants received tool-based training and two received narrative-based
training. Results showed that the Narrative-Based team recognized the
spatial-temporal relationship between events and constructed a timeline that
was a reasonable approximation of ground truth. In contrast, the Combined team
produced a linear sequence of events that did not encompass the relationships
between different adversaries. Finally, the Tool-Based team demonstrated little
appreciation of either the spatial or temporal relationships between events.
These findings suggest that participants receiving Narrative-Based training
were able to use the software tools in a way that allowed them to gain a
greater level of situation awareness. Keywords: cyber security; training; situational awareness | |||
| Instrumenting a Perceptual Training Environment to Support Dynamic Tailoring | | BIBAK | Full-Text | 100-109 | |
| Robert E. Wray; Jeremiah T. Folsom-Kovarik; Angela Woods | |||
| Simulation-based practice environments would be more valuable for learning
if they supported adaptive, targeted responses to students as they proceed thru
the experiences afforded by the environment. However, many adaptation
strategies require a richer interpretation of the student's actions and
attitudes than is available thru the typical simulation interface. Further,
creating extended interfaces for a single application solely to support
adaptation is often cost-prohibitive. In response, we are developing "learner
instrumentation middleware" that seeks to provide a generalized representation
of learner state via reusable algorithms, design patterns, and software. Keywords: Perceptual learning; adaptive training; learner modeling | |||
| Improving Tool Support for Software Reverse Engineering in a Security Context | | BIBAK | Full-Text | 113-122 | |
| Brendan Cleary; Christoph Treude; Fernando Figueira Filho; Margaret-Anne Storey; Martin Salois | |||
| Illegal cyberspace activities are increasing rapidly and many software
engineers are using reverse engineering methods to respond to attacks. The
security-sensitive nature of these tasks, such as the understanding of malware
or the decryption of encrypted content, brings unique challenges to reverse
engineering: work has to be done offline, files can rarely be shared, time
pressure is immense, and there is a lack of tool and process support for
capturing and sharing the knowledge obtained while trying to understand
assembly code. To help us gain an understanding of this reverse engineering
work, we conducted an exploratory study at a government research and
development organization to explore their work processes, tools, and artifacts
[1]. We have been using these findings to improve visualization and
collaboration features in assembly reverse engineering tools. In this talk, we
will present a review of the findings from our study, and present prototypes we
have developed to improve capturing and sharing knowledge while analyzing
security concerns. Keywords: malware; reverse engineering; empirical study | |||
| Brain Biomarkers of Neural Efficiency during Cognitive-Motor Performance: Performing under Pressure | | BIBAK | Full-Text | 123-132 | |
| Michelle E. Costanzo; Bradley D. Hatfield | |||
| The concept of neural efficiency provides a powerful framework to assess the
underlying mechanisms of brain dynamics during cognitive-motor performance.
Electroencephalography (EEG) studies have revealed that as cognitive-motor
performance improves non-essential brain processes are progressively disengaged
resulting in brain dynamics leading to a state of neural efficiency. Multiple
factors such as practice, genetics, mental stress, physical fitness and social
interaction (team dynamics) can influence such cortical refinements positively
or negatively and translate into an enhanced or deteriorated quality of
performance. This paper provides a report of brain activity, assessed via fMRI,
in a group of athletes who perform well under conditions of mental stress.
Better understanding of brain states associated with such groups can enhance
the ability to detect and classify adaptive mental states and increase the
possibility of employing field-friendly brain monitoring tools such as EEG in
ecologically valid situations for assessment of cognitive-motor performance in
challenging real-world settings. Keywords: Neural efficiency; expertise; fMRI; emotion regulation | |||
| The Geometry of Behavioral and Brain Dynamics in Team Coordination | | BIBA | Full-Text | 133-142 | |
| Silke Dodel; Emmanuelle Tognoli; J. A. Scott Kelso | |||
| Performing a task as a team requires that team members mutually coordinate their actions. It is this coordination that distinguishes the performance of a team from the same actions performed independently. Here we set out to identify signatures of team coordination in behavioral and brain dynamics. We use dual electroencephalography (EEG) to measure brain dynamics of dyadic teams performing a virtual room clearing task. Such complex tasks often exhibit high variability of behavioral and brain dynamics. Although such variability is often considered to impede identification of the behavior or brain dynamics of interest here we present a conceptual and empirical framework which explains variability in geometrical terms and classifies its sources into those that are detrimental and non-detrimental to performing the task at hand. Using our framework we found that behaviorally team coordination is reflected in terms of role dependent behavior. Furthermore we identified a low-dimensional subspace of the brain dynamics in the frequency domain which is specific for team behavior and correlated with successful team coordination. Moreover, successful team coordination was positively correlated with the inter- but not intra-brain coherence in the gamma band. Our results hence indicate that successful team coordination is associated with increased team cognition, particularly readiness to engage in the task. | |||
| Analysis of Semantic Content and Its Relation to Team Neurophysiology during Submarine Crew Training | | BIBAK | Full-Text | 143-152 | |
| Jamie C. Gorman; Melanie J. Martin; Terri A. Dunbar; Ronald H. Stevens; Trysha Galloway | |||
| A multi-level framework for analyzing team cognition based on team
communication content and team neurophysiology is described. The semantic
content of team communication in submarine training crews is quantified using
Latent Semantic Analysis (LSA), and their team neurophysiology is quantified
using the previously described neurophysiologic synchrony method. In the
current study, we validate the LSA communication metrics by demonstrating their
sensitivity to variations in training segment and by showing that less
experienced (novice) crews can be differentiated from more experienced crews
based on the semantic relatedness of their communications. Cross-correlations
between an LSA metric and a team neurophysiology metric are explored to examine
fluctuations in the lead-lag relationship between team communication and team
neurophysiology as a function of training segment and level of team experience.
Finally, the implications of this research for team training and assessment are
considered. Keywords: Latent Semantic Analysis; Team cognition; Team communication; Team
neurophysiology; Teamwork | |||
| Neurophysiological Predictors of Team Performance | | BIBAK | Full-Text | 153-161 | |
| Robin R. Johnson; Chris Berka; David Waldman; Pierre Balthazard; Nicola Pless; Thomas Maak | |||
| Objective: To identify benchmark neurophysiological measures that predict
performance at a teaming level. Advanced Brain Monitoring has a track record of
success in identifying neurophysiological metrics that impact expert behavior.
For example, we characterized negative and positive predictors for marksmanship
skill; persons with higher HF:LF Norm metrics of Heart rate variability (HRV,
an indication of anxiety) during a benchmarking auditory passive vigilance task
did not achieve expert marksman performance while those with above average
visuospatial processing ability achieved greater levels of expertise. In the
current research, we explored the ability of benchmark neurophysiological
metrics to predict team performance in two large scale studies. Significance:
Identifying neurophysiological metrics of teaming ability and performance as
part of a team can provide potential screening mechanisms or developmental data
to help build optimal teams and improve team interactions for different types
of contexts in which teams may operate. Keywords: leadership; neurophysiology; qEEG; prediction | |||
| How Long Is the Coastline of Teamwork? | | BIBAK | Full-Text | 162-171 | |
| John Kolm; Ronald Stevens; Trysha Galloway | |||
| A five-state Markov model is proposed for group and team operation and
evolution that has a stronger basis in neurodynamics, greater descriptive
accuracy and higher predictive value than many existing models. The derivation
of this model from the symbolic analysis of normalized EEG activity during
assigned team and group tasks is discussed, as are observations on team and
group dynamics which emerge from the model. The predictive value of the model
is shown when applied to independent data from submarine crew evolutions.
Observations are offered on team dynamics which show the five-state model and
its accompanying state transitions to be necessary and sufficient to describe
both linear and non-linear team dynamics, and to begin unifying these
traditional and new approaches in a straightforward way. Keywords: nonlinear dynamics; neurodynamics teamwork; markov model; state transition;
EEG symbol; tuckman | |||
| Effects of Teamwork versus Group Work on Signal Detection in Cyber Defense Teams | | BIBAK | Full-Text | 172-180 | |
| Prashanth Rajivan; Michael Champion; Nancy J. Cooke; Shree Jariwala; Genevieve Dube; Verica Buchanan | |||
| Cyber security is critical for any modern day organization's operations.
Organizational structure and reward policies not conducive for teamwork may be
affecting the performance of cyber defense analysts. Past research shows that
team interaction could lead to better cyber defense performance. However, the
value of team work in the cyber defense context has not been demonstrated using
empirical methodologies. To explore this, we conducted a study on the effects
of teamwork versus group work (i.e., looking at both the team and individual
levels) on signal detection performance of cyber security defense analysts
using the synthetic task environment called CyberCog. The results from the
preliminary analysis conducted reveal that simply encouraging analysts to work
as a team and providing team-level rewards leads to better team performance in
cyber defense analysis. Keywords: Cyber Defense Performance; Team Cognition; Team Performance; Synthetic Task
Environment; CyberCog | |||
| Developing Methodology for Experimentation Using a Nuclear Power Plant Simulator | | BIBA | Full-Text | 181-188 | |
| Lauren Reinerman-Jones; Svyatoslav Guznov; Joseph Mercado; Amy D'Agostino | |||
| Many of today's most complicated systems are human-machine systems that involve extensive advanced technology and a team of highly trained operators. As these human-machine systems are so complex, it is important to understand the factors that influence operator performance, operator state (e.g., overloaded, underload, stress) and the types of errors that operators make. Thus, it is desirable to develop an experimental methodology for studying complex systems that involve team operations. This paper looks at Nuclear Power Plant (NPP) operations as a test case for building this methodology. The methodology will reference some aspects/details specific to NPPs, but the general principles are intended to extend to any complex system that involves team operations. | |||
| Modeling Complex Tactical Team Dynamics in Observed Submarine Operations | | BIBAK | Full-Text | 189-198 | |
| Tara Smallidge; Eric Jones; Jerry Lamb; Rachel Feyre; Ronald Steed; Abaigeal Caras | |||
| Successful submarine operations -- those that accomplish the mission while
maintaining security and safety -- depend on numerous factors. Among the most
critical elements driving success are the effectiveness of team behavior and
the ability to understand when this behavior breaks down such that this
degradation can be mitigated or avoided. While underway, submarine Commanders
and other leaders must be attuned and alert to potential precursors that may
manifest in decreased performance. This paper describes a framework used to
develop performance measures to support formative assessment of team behaviors
and to examine team breakdown and degradation. Results are reported from two
events: an observation of an operational exercise and a study at the Naval
Submarine School concerning the validity and utility of the measures. This
preliminary research captured essential aspects of performance and helped
define future efforts to develop better tools for assessing team behavior and
understanding team breakdown in our warfighters. Keywords: performance measures; formative assessment; team effectiveness; team
breakdown; submarine | |||
| How Tasks Help Shape the Neurodynamic Rhythms and Organizations of Teams | | BIBAK | Full-Text | 199-208 | |
| Ronald Stevens; Trysha Galloway; Gwendolyn Campbell; Chris Berka; Pierre Balthazard | |||
| We have modeled neurophysiologic indicators of Engagement and Workload to
determine the influence the task has on the resulting neurodynamic rhythms and
organizations of teams. The tasks included submarine piloting and navigation
and anti-submarine warfare military simulations, map navigation tasks for high
school students and business case discussions for entrepreneurial / corporate
teams. The team composition varied from two to six persons and all teams had
teamwork experience with the tasks. For each task condition teams developed
task-specific neurodynamic rhythms. These task-specific rhythms were present
during much of the task but could be interrupted by exogenous or endogenous
disturbances to the team or environment. The effects of these disturbances
could be rapidly detected by changes in the entropy levels of the team
neurodynamics symbol streams. These results suggest the possibility of
performing task-specific comparisons of the rhythms and organizations across
teams expanding the opportunities for rapid detection of less than successful
performances and targeted interventions. Keywords: team neurodynamics; entropy; coordination dynamics; rhythms | |||
| Neurophysiological Estimation of Team Psychological Metrics | | BIBAK | Full-Text | 209-218 | |
| Maja Stikic; Chris Berka; David Waldman; Pierre Balthazard; Nicola Pless; Thomas Maak | |||
| The goal of this study was to explore the feasibility of continuous
neurophysiological assessment of different psychological aspects of a team
process. The teams consisted of the MBA students who discussed and attempted to
solve a case problem dealing with corporate social responsibility (i.e. child
labor). At the end of the team process, two types of psychological metrics
(i.e., engagement and leadership) were assessed by team members, both at the
individual and team levels. These metrics showed significant correlations with
the team performance scores derived by four trained coders. Two of them rated
the teams' solutions in terms of effective problem solving, decisiveness, and
creativity. The other two coders rated the level of moral reasoning displayed
in the solutions. The psychological metrics were then estimated based on
quantitative electroencephalography (qEEG). Different modeling techniques, such
as linear and quadratic discriminant function analysis (DFA) and linear
regression were applied to the processed qEEG data. The models were evaluated
through auto-validation, but also through cross-validation to test stability of
the models in the team-independent training setting. The experimental results
suggested that qEEG could be effectively used in the team settings as an
estimator of individual and team engagement, as well as the leadership
qualities shown by team members. Our findings suggest that qEEG can help in
understanding, and perhaps building, optimal teams and team processes. Keywords: team process; engagement; leadership; electroencephalography | |||
| Physio-behavioral Coupling as an Index of Team Processes and Performance: Overview, Measurement, and Empirical Application | | BIBAK | Full-Text | 219-228 | |
| Adam J. Strang; Gregory J. Funke; Sheldon M. Russell; Robin D. Thomas | |||
| Research shows that teammates often exhibit similarity in their
physiological and behavioral responses during cooperative task performance, a
phenomenon referred to here as physio-behavioral coupling (PBC). Goals of this
manuscript are to provide an overview of research examining the utility of PBC
as an index of team processes (e.g., coordination) and performance, discuss
applied and theoretical issues in PBC measurement, and present findings from a
study using linear and nonlinear statistics to assess PBC. Keywords: Team; Coupling; Coordination; Performance; Nonlinear | |||
| Combined Linear Regression and Quadratic Classification Approach for an EEG-Based Prediction of Driver Performance | | BIBAK | Full-Text | 231-240 | |
| Gregory Apker; Brent Lance; Scott Kerick; Kaleb McDowell | |||
| Electroencephalography (EEG) has been used to reliably and non-invasively
detect fatigue in drivers. In fact, linear relationships between EEG
power-spectral estimates and indices of driver performance have been found
during simplified driving tasks. Here we sought to predict driver performance
using linear regression in a more complex paradigm. Driver performance varied
widely between participants, often varying greatly within a single driving
session. We found that a non-selective linear regression model did not
generalize well between periods of stable and erratic driving, yielding large
errors. However, prediction errors were significantly reduced by training a
linear regression model on stable driving for each participant. To provide a
confidence estimate for the stable driving model, a quadratic discriminate
classifier was trained to detect the transition from stable to erratic driving
from the EEG power-spectra. Combined, the regression model and classifier
yielded significantly lower prediction errors and provided improved
discrimination of poor driving. Keywords: EEG; Regression; Driving; Fatigue; Power Spectral Density | |||
| Differential Prefrontal Response during Natural and Synthetic Speech Perception: An fNIR Based Neuroergonomics Study | | BIBAK | Full-Text | 241-249 | |
| Hasan Ayaz; Paul Crawford; Adrian Curtin; Mashaal Syed; Banu Onaral; Willem M. Beltman; Patricia A. Shewokis | |||
| Synthetic speech has a growing role in human computer interaction and
automated systems with the emergence of ubiquitous computing such as smart
phones, car multimedia control and navigation systems. Cognitive processing
costs associated with comprehension of synthetic speech relative to
comprehension of natural speech have been demonstrated with behavioral
(reaction time, accuracy, etc.) and self-reported (ratings, etc.) measures. In
this neuroergonomics study, we have used optical brain imaging (fNIR:
functional near infrared spectroscopy) to capture the brain activation of
participants while they were listening to speech with varied quality, as well
as natural speech. Results indicated a differential hemodynamic response with
speech quality. As fNIR systems are safe, portable and record brain activation
in real world settings, fNIR is a practical and minimally intrusive assessment
tool for user experience researchers and can provide an objective metric for
the design and development of next generation synthetic speech systems. Keywords: Optical Brain Imaging; functional near infrared spectroscopy; fNIR;
synthetic speech; perception; auditory processing | |||
| Functional Near-Infrared Spectroscopy in Addiction Treatment: Preliminary Evidence as a Biomarker of Treatment Response | | BIBAK | Full-Text | 250-258 | |
| Scott C. Bunce; Jonathan Harris; Kurtulus Izzetoglu; Hasan Ayaz; Meltem Izzetoglu; Kambiz Pourrezaei; Banu Onaral | |||
| There is growing evidence that there are functional changes in the brains of
individuals with substance use disorders. Numerous studies utilizing functional
magnetic resonance imaging (fMRI) have shown that drug cues elicit increased
regional blood flow in reward-related brain areas among addicted participants
that is not found among normal controls. This finding has prompted leading
investigators to suggest fMRI might be useful as a diagnostic or prognostic
biomarker of addiction severity. However, fMRI is too costly for routine use in
most treatment facilities. Functional near-infrared spectroscopy (fNIRs) offers
an alternative neuroimaging modality that is safe, affordable, and
patient-friendly. This manuscript reviews evidence that fNIRs can be used to
differentiate prefrontal cortical responses of current alcohol dependent
participants from alcohol dependent patients in treatment for 90-180 days.
Differential responses to both alcohol and natural reward cues in both groups
suggests fNIRs might serve as a clinic-friendly neuroimaging technology to
inform clinical practice. Keywords: Addiction; alcoholism; neuroimaging; functional near infrared spectroscopy;
fNIRs; functional magnetic resonance imaging; fMRI; biomarker | |||
| Towards Noise-Enhanced Augmented Cognition | | BIBAK | Full-Text | 259-268 | |
| Alexander J. Casson | |||
| Workload classification Augmented Cognition systems aim to detect when an
operator is in a high or low workload state, and then to modify their work flow
and operating environment based upon this knowledge. This paper reviews
state-of-the-art electroencephalography (EEG) recorders for use in such systems
and investigates the impact of EEG noise on an example system performance. It
is found that adding up to 15 µVRMS of artificially generated noise
still leaves EEG signals that have correlations in-line with the correlations
found between conventional wet EEG electrodes and new dry electrodes. The
workload classification system is found to be robust in the presence of small
amounts of noise, and there is initial evidence of small stochastic resonance
effects whereby better performance can actually be obtained in the noisy case
compared to the traditional noise-less case. Keywords: EEG; Augmented Cognition; Workload classification; Noise-enhanced signal
processing | |||
| Soft, Embeddable, Dry EEG Sensors for Real World Applications | | BIBAK | Full-Text | 269-278 | |
| Gene Davis; Catherine McConnell; Djordje Popovic; Chris Berka; Stephanie Korszen | |||
| Over the last decade, numerous papers have presented the use of dry
electrodes capable of acquiring electroencephalogram (EEG) signals through
hair. A few of these dry electrode prototypes have even progressed from
lab-based EEG acquisition to commercial sales. While the field has improved
rapidly as of late, most dry electrodes share a number of shortcomings that
limit their potential real world applications including: 1) multiple rigid
prongs that require sustained pressure to penetrate hair and maintain solid
scalp contact, creating higher levels of discomfort when compared to standard
wet sensors; 2) cumbersome or chin-strap-type applications for maintaining
electrode contact, creating barriers to end user acceptance; 3) rigid active
electrodes to compensate for high input impedances that limit flexibility and
placement of sensors; 4) inability to safely imbed sensors under protective
headgear, restricting use in some fields where EEG metrics are most desired;
and 5) expensive sensor manufacturing that drives costs high for use across
subjects. Under a recent DARPA Phase 3 contract, Advanced Brain Monitoring has
developed a novel semi-dry sensor that addresses the current dry electrode
shortcomings, opening up the door for new real world applications without
compromising subject safety or comfort. The semi-dry sensor prototype was
tested during a live performance requirement at the end of Phase 3, and
successfully acquired EEG across all subject hair types over a 3 day testing
period. The results from the performance requirement and subsequent results for
new advancements to the prototype are presented here. Keywords: Electroencephalograms (EEG); dry-electrodes; wearable EEG; BCI; Real World
Applications | |||
| Real-Time Workload Assessment as a Foundation for Human Performance Augmentation | | BIBAK | Full-Text | 279-288 | |
| Kevin Durkee; Alexandra Geyer; Scott Pappada; Andres Ortiz; Scott Galster | |||
| While current military systems are functionally capable of adaptively aiding
human operators, the effectiveness of this capability depends on the
availability of timely, reliable assessments of operator states to determine
when and how to augment effectively. This paper describes a response to the
technical challenges associated with establishing a foundation for reliable and
effective adaptive aiding technologies. The central component of this approach
is a real-time, model-based classifier and predictor of operator state on a
continuous high resolution (0-100) scale. Using operator workload as a test
case, our approach incorporates novel methods of integrating physiological,
behavioral, and contextual factors for added precision and reliability.
Preliminary research conducted in the Air Force Multi Attribute Task Battery
(AF_MATB) illustrates the added value of contextual and behavioral data for
physiological-derived workload estimates, as well as promising trends in the
classification accuracy of our approach as the basis for employing adaptive
aiding strategies. Keywords: Workload; Augmentation; Human Performance; Modeling and Simulation;
Physiological Measurement | |||
| Using the EEG Error Potential to Identify Interface Design Flaws | | BIBAK | Full-Text | 289-298 | |
| Jeff Escalante; Serena Butcher; Mark R. Costa; Leanne M. Hirshfield | |||
| There are a number of limitations to existing usability testing methods,
including surveys, interviews, talk-alouds, and participant observations. These
limitations include subject bias, poor recall, and inability to capture
fleeting events, such as when a UI functions or behaves in a manner that
contradicts user expectations. One possible solution to these problems is to
use electrophysiological indicators to monitor user interaction with the UI. We
propose using event related potentials (ERP), and the error potential (ErrP)
more specifically, to capture moment-to-moment interactions that lead to
violations in user expectations. An ERP is a response generated in the brain to
stimuli, while the ErrP is a more specific signal shown to be elicited by
subject error. In this experiment we monitored subjects using a 10-channel
electroencephalogram (EEG) as they completed a range of simple web browsing
tasks. However, roughly 1/3 of the time subjects were confronted with poor UI
design features (e.g., broken links). We then used statistical and machine
learning techniques to classify the data and found that we were able to
accurately identify the presence of error potentials. Furthermore, the ErrP was
present when the subjects encountered a UI design flaw, but only during the
more 'overt' examples of our design flaws. Results support our hypothesis that
ERPs and ErrPs, can be used to identify UI design flaws for a variety of
systems, from web sites to video games. Keywords: EEG; usability testing; error potential | |||
| An Effective ERP Model for Brain Computer Interface | | BIBAK | Full-Text | 299-307 | |
| Mariko Funada; Yoshihide Igarashi; Tadashi Funada; Miki Shibukawa | |||
| The investigation of BCI (Brain Computer Interface) is particularly
interesting for HCI research. Some of recent results concerning BCI have much
contributed to the progress of HCI. In this paper we propose an effective ERP
model that can reduce the difference among individuals in the process of
repetitive tasks. Human brain reactions are quantified by ERPs (Event Related
Potentials) that reflect the change of brain reactions through repetitive
tasks. We discuss a method of how to even out the difference appeared in ERPs
among individuals. Keywords: BCI; EEG; ERP; Individual difference; Model | |||
| Neural Oscillatory Signature of Original Problem Solving | | BIBAK | Full-Text | 308-315 | |
| Henk J. Haarmann; Polly O'Rourke; Timothy George; Alexei Smaliy; Kristin Grunewald; Joseph Dien | |||
| The goal of the present research was to increase understanding of the neural
oscillatory signature of originality in verbal divergent thinking by
determining if event-related synchronization (ERS) in frequency bands other
than alpha predicts originality. EEG was recorded while participants performed
the insight task in which they were presented with a brief scenario and asked
to generate as many explanations as possible during a three minute period.
After the EEG session, participants were asked to rate the originality of each
idea they produced. Analyses revealed that high originality was associated with
decreases in the high beta ERS and with hemispheric asymmetry in the low beta
band, immediately prior to idea generation. These results suggest the neural
signature of originality extends beyond hemispheric asymmetries in the alpha
band and provide important insights into the neural underpinnings of verbal
creativity. Keywords: Divergent thinking; originality; EEG; ERS; alpha; beta | |||
| A Real-World Neuroimaging System to Evaluate Stress | | BIBAK | Full-Text | 316-325 | |
| Bret Kellihan; Tracy Jill Doty; W. David Hairston; Jonroy Canady; Keith W. Whitaker; Chin-Teng Lin; Tzyy-Ping Jung; Kaleb McDowell | |||
| While the laboratory setting offers researchers a great deal of experimental
control, this environment also limits how generalizable the results are to the
real world. This is particularly true when studying the multifaceted phenomenon
of stress, which often relies on personal experience, a dimension that is
difficult to reproduce in the laboratory setting. This paper describes a novel,
multi-aspect real-world integrated neuroimaging system (MARIN) optimized to
study physiological phenomena in the real-world and particularly suited to the
study of stress. This system integrates neurological data from a gel-free,
wireless EEG device with physiological data from wireless cardiac and skin
conductance sensors, as well as self-reports of activity and stress.
Coordination of the system is managed through an Android handheld mobile device
that also logs salient events and presents inventories for subjective reports
of stress. The integration of these components creates a rich, multimodal
dataset with minimal interference to the user's daily life, and these data will
guide the further understanding of neurological mechanisms of stress. Keywords: wireless electroencephalography; skin conductance response; electrodermal
activation; heart-rate variability; wearability | |||
| Optimal Feature Selection for Artifact Classification in EEG Time Series | | BIBAK | Full-Text | 326-334 | |
| Vernon Lawhern; W. David Hairston; Kay Robbins | |||
| Identifying artifacts or non-brain electrical signals in EEG time series is
often a necessary but time-consuming preprocessing step, as many EEG analysis
techniques require that the data be artifact free. Because of this, reliable
and accurate techniques for automated artifact detection are desirable in
practice. Previous research has shown that coefficients obtained from
autoregressive (AR) models can be used as feature vectors to classify among
several different artifact conditions found in EEG. However, a statistical
method for identifying significant AR features has not been presented. In this
work we propose a method for determining the optimal AR features that is based
on penalized multinomial regression. Our results indicate that the size of the
feature vector can be greatly reduced with minimal loss to classification
accuracy. The features selected by this algorithm localize to specific channels
and suggests a possible BCI implementation with increased computational
efficiency than with using all available channels. We also show that the
significant AR features produced by this approach correlate to known brain
physiological properties. Keywords: Autoregressive (AR) model; Artifacts; Electroencephalography;
classification; feature selection; multinomial regression; penalized
regression; machine learning | |||
| Towards a Hybrid P300-Based BCI Using Simultaneous fNIR and EEG | | BIBAK | Full-Text | 335-344 | |
| Yichuan Liu; Hasan Ayaz; Adrian Curtin; Banu Onaral; Patricia A. Shewokis | |||
| Next generation brain computer interfaces (BCI) are expected to provide
robust and continuous control mechanism. In this study, we assessed integration
of optical brain imaging (fNIR: functional near infrared spectroscopy) to a
P300-BCI for improving BCI usability by monitoring cognitive workload and
performance. fNIR is a safe and wearable neuroimaging modality that tracks
cortical hemodynamics in response to sensory, motor, or cognitive activation.
Eight volunteers participated in the study where simultaneous EEG and 16 optode
fNIR from anterior prefrontal cortex were recorded while participants engaged
with the P300-BCI for spatial navigation. The results showed a significant
response in fNIR signals during high, medium and low performance indicating a
positive correlation between prefrontal oxygenation changes and BCI
performance. This preliminary study provided evidence that the performance of
P300-BCI can be monitored by fNIR which in turn can help improve the robustness
of the BCI classification. Keywords: BCI; P300; fNIR; Performance; Optical brain imaging; EEG | |||
| A Novel Method for Single-Trial Classification in the Face of Temporal Variability | | BIBAK | Full-Text | 345-352 | |
| Amar R. Marathe; Anthony J. Ries; Kaleb McDowell | |||
| Machine learning techniques have been used to classify patterns of neural
data obtained from electroencephalography (EEG) to increase human-system
performance. This classification approach works well in controlled laboratory
settings since many of the machine learning techniques used often rely on
consistent neural responses and behavioral performance over time. Moving to
more dynamic, unconstrained environments, however, introduces temporal
variability in the neural response resulting in sub-optimal classification
performance. This study describes a novel classification method that accounts
for temporal variability in the neural response to increase classification
performance. Specifically, using sliding windows in hierarchical discriminant
component analysis (HDCA), we demonstrate a decrease in classification error by
over 50% when compared to other state-of-the-art classification methods. Keywords: Brain-Computer Interface (BCI); Rapid Serial Visual Presentation (RSVP);
Electroencephalography (EEG); HDCA; Sliding HDCA; Temporal Variability;
Single-trial; Real-world environment | |||
| A Translational Approach to Neurotechnology Development | | BIBAK | Full-Text | 353-360 | |
| Kaleb McDowell; Anthony Ries | |||
| The past several decades have seen an explosion of meaningful and nuanced
insights into the connection between human behavior and the nervous system;
however, the translation of these insights into viable applications is a
non-trivial and widely acknowledged challenge. Recent advancements in
brain-computer interaction and real-world neuroimaging technologies have
provided major breakthroughs that provide the underpinnings for translational
neuroscience research efforts. This session focuses on building off of those
advancements and specifically proposes three concepts necessary for overcoming
the challenges of translation: 1) integrating aspects of knowledge of brain
function that are generally separate into single analyses, 2) increasing
situational complexity, and 3) continuing to develop neuroimaging tools
specifically for use in real-world environments. Keywords: Translational Neuroscience; Neurotechnology; Brain-Computer Interface (BCI);
Electroencephalography (EEG); Neural Classification | |||
| Understanding Brain Connectivity Patterns during Motor Performance under Social-Evaluative Competitive Pressure | | BIBAK | Full-Text | 361-370 | |
| Hyuk Oh; Rodolphe J. Gentili; Michelle E. Costanzo; Ronald N. Goodman; Li-Chuan Lo; Jeremy C. Rietschel; Mark Saffer; Bradley D. Hatfield | |||
| Previous studies have shown that psychological arousal impacts motor
performance during social-evaluative tasks by its influence on cortical
dynamics, which can translate into motor performance enhancement. Although
these findings have established critical links between performance under mental
stress and elevated brain activity beyond that required for performance, there
is still a need to further investigate brain connectivity during cognitive
motor performance under such conditions. Here both electroencephalographic
(EEG) and shooting performance were obtained in a shooting task under both
performance-alone and competitive conditions. Network connectivity was assessed
for the localized EEG sources. The results are consistent with those previously
obtained and suggest elevated statistical dependencies and causal interactions
between motor and non-motor areas during the competitive condition relative to
performance-alone. Such network analysis provides a complementary approach to
more traditional EEG derived metrics allowing for examining brain dynamics
during cognitive motor performance under varying conditions of mental stress. Keywords: Brain connectivity; EEG Localization; Motor Cognition; Competitive Pressure Note: Best paper award | |||
| Removal of Ocular Artifacts from EEG Using Learned Templates | | BIBAK | Full-Text | 371-380 | |
| Max Quinn; Santosh Mathan; Misha Pavel | |||
| Electroencephalogram (EEG) data can provide information on cognitive states
and processes with high temporal resolution, but to take full advantage of this
temporal resolution, common transients such as blinks and eye movements must be
accounted for without censoring data. This can require additional hardware,
large amounts of data, or manual inspection. In this paper we introduce a
greedy, template-based method for modeling and removing transient activity. The
method iteratively models an input and updates a template; a process which
quickly converges to a unique and efficient approximation of the input. When
combined with standard source separation techniques such as Independent
Component Analysis (ICA) or Principal Component Analysis (PCA), the method
shows promise for the automatic and data driven removal of ocular artifacts
from EEG data. In this paper we outline our method, provide evidence for its
effectiveness using synthetic EEG data, and demonstrate its effect on real EEG
data recorded as part of a minimally constrained cognitive task. Keywords: EEG; EOG; ICA; PCA; BCI; matching pursuit | |||
| Brain in the Loop Learning Using Functional Near Infrared Spectroscopy | | BIBAK | Full-Text | 381-389 | |
| Patricia A. Shewokis; Hasan Ayaz; Adrian Curtin; Kurtulus Izzetoglu; Banu Onaral | |||
| The role of practice is crucial in the skill acquisition process and for
assessments of learning. In this study, we used a portable neuroimaging
technique, functional near infrared (fNIR) spectroscopy for monitoring
prefrontal cortex activation during learning of spatial navigation tasks
throughout 11 days of training and testing. Two different tasks orders, blocked
and random, were used to test the effect of the practice schedule on the
acquisition and transfer of 3D computer mazes. Results indicated variable
decreases in the hemodynamic response during the initial days of practice.
Although there were no differences in mean oxygenation for the practice orders
across acquisition the random practice order used less oxygenation than the
blocked order for the more difficult tasks in the transfer phase Use of brain
activation and behavioral measures provides can provide a more accurate
depiction of the learning process. Since fNIR systems are safe, portable and
record brain activation in ecologically valid settings, fNIR can contribute to
future learning settings for assessment and personalization of the training
regimen. Keywords: Optical Brain Imaging; functional near infrared spectroscopy; fNIR;
Learning; Spatial navigation; contextual interference | |||
| Brain Activity Based Assessment (BABA) | | BIBAK | Full-Text | 390-398 | |
| Roy Stripling; Grace Chang | |||
| Event-Related Potentials (ERP) are changes in brain activity detected using
electroencephalographic (EEG) methods. One well-studied ERP is the P3b, which
is generally elicited by asking participants to press a key when presented a
target stimulus (e.g., "T") that is intermixed with a much more commonly
presented non-target stimulus (e.g., "S"). We hypothesized that we could assess
knowledge by asking participants to solve a problem then press a key when they
see the correct answer in a series of (mostly wrong) answers. Early pilot
testing (four participants) suggests that the P3b shows promise in this regard.
In a math test, P3b responses were produced when shown correct, but not
incorrect answers. In a foreign-language vocabulary test (matching picture to
foreign word), P3b responses were not produced when shown correct answers prior
to studying the words, but did produce P3b responses after studying. Some
notable deviations in individual participants are discussed. Keywords: Evoked Potential; Electroencephalogram; EEG; Knowledge Assessment | |||
| Enhancing Intuitive Decision Making through Implicit Learning | | BIBAK | Full-Text | 401-409 | |
| Joseph Cohn; Peter Squire; Ivy Estabrooke; Elizabeth O'Neill | |||
| Today's military missions pose complex time-constrained challenges, such as
detecting IED emplacements while in a moving vehicle or detecting anomalous
civilian behaviors indicative of impending danger. These challenges are
compounded by recent doctrinal requirements that require younger and
less-experienced Warfighters to make ever-more complex decisions. Current
understanding of decision making, which is based on concepts developed around
theories of analytic decision making (Newell and Simon, 1972), cannot
effectively address these new challenges since they are based on the notion of
enabling experts to apply their expertise to addressing new problems. Yet,
there are actually two types of recognized decision making processes,
analytical and intuitive, which appear to be mediated by different processes or
systems (Ross et al, 2004; Evans, 2008; Kahneman & Klein, 2009). Analytical
decision making is mediated by processes that reflect a sequential,
step-by-step, methodical, and time-consuming process. To be effective, analytic
decision making appears to require domain expertise. In contrast, intuitive
decision making relies upon a more holistic approach to processing information
at a subconscious level (Luu et al, 2010). The thesis of this paper is that
unlike analytic decision making, effective intuitive decision making does not
require domain expertise but, rather, can be enhanced through training methods
and technologies. This paper will explore ways in which the results from a
range of studies at the behavioral, cognitive and neurophysiological levels can
be leveraged to provide a comprehensive approach to understanding and enabling
more effective intuitive decision-making for these non-experts. Keywords: Cognitive Modeling; Perception; Emotion and Interaction; Intuition Decision
Making; Implicit Learning | |||
| Measuring Engagement to Stimulate Critical Thinking | | BIBAK | Full-Text | 410-417 | |
| Patricia J. Donohue; Tawnya Gray; Dominic Lamboy | |||
| This research is a theoretical study of game-augmented instruction for
learning and playing mathematics challenges. We wanted to extend our work with
a unique Studio-Based Learning (SBL) model for peer-critiques of project
designs. SBL had been used successfully in 15 universities as an approach for
helping undergraduate computer science students improve their programming
skills and code reviews. We piloted the model in a 9th-grade spatial studies
class with some success in teaching freshmen how to critique their work and
participate in peer reviews across teams. From those experiences we developed a
framework for an interactive mobile application of the studio experience.
Research with a group of student athletes revealed that before mobile
development, we needed to consider the constraints of learner characteristics
on the mobile environment. This study sets out the design for a pilot test of
our finding that learning style may drive game features for instruction. Keywords: Mobile Learning; Mathematics; Physiological Measurement; Engagement;
Physical Cognition; Game Theory | |||
| Human Dimension in Cyber Operations Research and Development Priorities | | BIBA | Full-Text | 418-422 | |
| Chris Forsythe; Austin Silva; Susan Stevens-Adams; Jeffrey Bradshaw | |||
| Within cyber security, the human element represents one of the greatest
untapped opportunities for increasing the effectiveness of network defenses.
However, there has been little research to understand the human dimension in
cyber operations. To better understand the needs and priorities for research
and development to address these issues, a workshop was conducted August 28-29,
2012 in Washington DC. A synthesis was developed that captured the key issues
and associated research questions.
Research and development needs were identified that fell into three parallel paths: (1) human factors analysis and scientific studies to establish foundational knowledge concerning factors underlying the performance of cyber defenders; (2) development of models that capture key processes that mediate interactions between defenders, users, adversaries and the public; and (3) development of a multi-purpose test environment for conducting controlled experiments that enables systems and human performance measurement. | |||
| Integration of Psycognitive States to Broaden Augmented Cognition Frameworks | | BIBAK | Full-Text | 423-432 | |
| Karmen Guevara | |||
| Augmented Cognition technologies focus on assessing and monitoring the user
to produce a composite picture of their cognitive state. This is based on the
mental processes of the user involving perception, memory, judgment and
reasoning. It does not include the emotional, volitional or the subconscious
processes. Due to the absence of input from a core human dimension -- the
subconscious -- it is inevitable that the picture to emerge from this data will
be incomplete. The focus of this paper therefore, is on this subconscious
dimension. The objective is to illustrate how subconscious processes can shape
behaviours and determine individuals' strategic actions. We argue that in order
to formulate a complete portrait of an individual's cognitive state, it is
important to integrate the subconscious dimension. Keywords: Psycognition; characterology; subconscious behaviours; character strategies;
critical incident breakdown; situational appropriate behaviour; inner
subjective domain | |||
| Human Performance Assessment Study in Aviation Using Functional Near Infrared Spectroscopy | | BIBAK | Full-Text | 433-442 | |
| Joshua Harrison; Kurtulus Izzetoglu; Hasan Ayaz; Ben Willems; Sehchang Hah; Hyun Woo; Patricia A. Shewokis; Scott C. Bunce; Banu Onaral | |||
| Functional near infrared (fNIR) spectroscopy is a field-deployable optical
neuroimaging technology that provides a measure of the prefrontal cortex's
cerebral hemodynamics in response to the completion of sensory, motor, or
cognitive tasks. Technologies such as fNIR could provide additional performance
metrics directly from brain-based measures to assess safety and performance of
operators in high-risk fields. This paper reports a case study utilizing a
continuous wave fNIR technology deployed in a real-time air traffic control
(ATC) setting to evaluate the cognitive workload of certified professional
controllers (CPCs) during the deployment of one of the Federal Aviation
Administration's (FAA's) Next Generation (NextGen) technologies. Keywords: Near-infrared spectroscopy; optical brain imaging; fNIR; human performance
assessment; air traffic control; workload | |||
| Robust Classification in RSVP Keyboard | | BIBAK | Full-Text | 443-449 | |
| Matt Higger; Murat Akcakaya; Umut Orhan; Deniz Erdogmus | |||
| To use in the Rapid Serial Visual Presentation (RSVP) Keyboard™, a
brain computer interface (BCI) typing system developed by our group, we propose
a robust classification method of handling non-stationarity in the
electroencephelography (EEG) data that is caused by artifacts and/or sensor
failure. Considering the effect of these non-stationarities, we build a mixture
data model to use as EEG evidence in the fusion with an n-gram language model
to develop a robust classification algorithm. Using Monte Carlo simulations on
the pre-recorded EEG data containing sections with or without intentionally
generated artifacts we compare the typing performances of non-robust and robust
classification methods in terms of speed and accuracy. Keywords: BCI; ERP; Spelling | |||
| Real-Time Vigilance Estimation Using Mobile Wireless Mindo EEG Device with Spring-Loaded Sensors | | BIBAK | Full-Text | 450-458 | |
| Li-Wei Ko; Chun-Hsiang Chuang; Chih-Sheng Huang; Yen-Hsuan Chen; Shao-Wei Lu; Lun-De Liao; Wan-Ting Chang; Chin-Teng Lin | |||
| Monitoring the neurophysiological activities of human brain dynamics in an
operational environment poses a severe measurement challenge using current
laboratory-oriented biosensor technology. The goal of this research is to
design, develop and test the wearable and wireless dry-electrode EEG
human-computer interface (HCI) that can allow assessment of brain activities of
participants actively performing ordinary tasks in natural body positions and
situations within a real operational environment. Its implications in HCI were
demonstrated through a sample application: vigilance-state prediction of
participants performing a realistic sustained-attention driving task. Besides,
this study further developed an online signal processing for extracting EEG
features and assessing cognitive performance. We demonstrated the feasibility
of using dry EEG sensors and miniaturized supporting hardware/software to
continuously collect EEG data recorded from hairy sites (i.e., occipital
region) in a realistic VR-based dynamic driving simulator. Keywords: Drowsy driving; Wireless and dry EEG device; Mindo; Human-computer interface | |||
| Relationship Analysis between Subjective Evaluation and NIRS-Based Index on Video Content | | BIBAK | Full-Text | 459-466 | |
| Shinsuke Mitsui; Atsushi Maki; Toshikazu Kato | |||
| Brain activities have been investigated, and various functions of brain have
been revealed recently. In our experiment, decrease of oxy-Hb change at frontal
cortex was observed while subjects were watching video contents. Also, the
degrees of decreases were different among the subjective evaluations about
impression against the video contents. Revealing the cause of the decrease has
the possibility to evaluate video content objectively. In this paper we discuss
the relationship between subjective evaluation and brain activity on video
content. Keywords: Frontal cortex; Near-infrared spectroscopy; Subjective evaluation; Video
content | |||
| Towards Evaluating Computational Models of Intuitive Decision Making with fMRI Data | | BIBAK | Full-Text | 467-473 | |
| James Niehaus; Victoria Romero; Avi Pfeffer | |||
| A vast array of everyday tasks require individuals to use intuition to make
decisions and act effectively, including civilian and military professional
tasks such as those undertaken by firefighters, police, search and rescue,
small unit leaders, and information analysts. To better understand and train
intuitive decision making (IDM), we envision future training systems will
represent IDM through computational models and use these models to guide IDM
learning. This paper presents the first steps to the problem of validating
computational models of IDM. To test if these models correlate with human
performance, we examine methods to analyze functional magnetic resonance
imaging (fMRI) data of human participants performing intuitive tasks. In
particular, we examine the use of a new deep learning representation called
sum-product networks to perform model-based fMRI analysis. Sum-product networks
have been shown to be simpler, faster, and more effective than previous deep
learning approaches, making them ideal candidates for this computationally
demanding analysis. Keywords: intuition; intuitive decision making; deep learning; sum-product network;
functional magnetic resonance imaging; model-based fMRI | |||
| Human Memory Systems: A Framework for Understanding the Neurocognitive Foundations of Intuition | | BIBAK | Full-Text | 474-483 | |
| Paul J. Reber; Mark Beeman; Ken A. Paller | |||
| A neurocomputational framework is described for characterizing how intuitive
and deliberate processing are accomplished in the human brain. The framework is
derived from memory systems theory and supported by research findings on
contrasts between implicit versus explicit (nonconscious versus conscious)
memory. Implicit intuition and deliberate deduction depend on separate types of
memory supported by distinct brain networks. For optimal decision making,
training should be designed to accommodate the operating characteristics of
both types of memory. Furthermore, reliance on explicit memory can inhibit the
use of implicit intuition, so training must facilitate effective interactions
between the two types of mechanism. To aid investigations of these effects, we
introduce a Mixture-of-Experts model that characterizes the interaction between
memory systems -- the PINNACLE model (Parallel Interacting Neural Networks
Competing in Learning). This model captures the separate neural networks that
reflect implicit and explicit processing, as well as their interaction, and it
can thus guide the development of training approaches to maximize the benefits
of concurrent use of both intuition and deliberation in decision making. Keywords: Intuition; decision making; implicit; explicit; memory systems; cognitive
neuroscience; cognitive modeling | |||
| Modeling Cues for Intuitive Sensemaking Simulations | | BIBAK | Full-Text | 484-491 | |
| Sae Schatz; Kathleen Bartlett | |||
| Modern military personnel must not only possess typical warfighting
abilities; they must also be able to rapidly perceive, understand, and then
respond to a range of ambiguous behavioral, social, and cultural stimuli. In
other words, personnel must have sociocultural sensemaking skills -- preferably
intuitive sensemaking skills that allow them to act with the utmost agility.
This paper begins by discussing sensemaking, sociocultural pattern recognition,
and expertise-based intuition. It briefly describes training approaches for
these constructs, as well as training for the integrated concept. Instructional
simulations could facilitate such training. However, for simulations to
effectively support this subject matter, they must be able to replicate
realistic patterns of life, from the subtle characteristics of human body
language to the emergent behaviors of crowds. That is, they must provide
accurate, nuanced cues to which the trainees can react. This paper closes by
discussing our ongoing work to address this gap by modeling realistic cues in a
simulation. Keywords: sensemaking; intuition; patterns of life; simulation; military training;
cognitive readiness | |||
| Evaluating Classifiers for Emotion Recognition Using EEG | | BIBA | Full-Text | 492-501 | |
| Ahmad Tauseef Sohaib; Shahnawaz Qureshi; Johan Hagelbäck; Olle Hilborn; Petar Jercic | |||
| There are several ways of recording psychophysiology data from humans, for example Galvanic Skin Response (GSR), Electromyography (EMG), Electrocardiogram (ECG) and Electroencephalography (EEG). In this paper we focus on emotion detection using EEG. Various machine learning techniques can be used on the recorded EEG data to classify emotional states. K-Nearest Neighbor (KNN), Bayesian Network (BN), Artificial Neural Network (ANN) and Support Vector Machine (SVM) are some machine learning techniques that previously have been used to classify EEG data in various experiments. Five different machine learning techniques were evaluated in this paper, classifying EEG data associated with specific affective/emotional states. The emotions were elicited in the subjects using pictures from the International Affective Picture System (IAPS) database. The raw EEG data were processed to remove artifacts and a number of features were selected as input to the classifiers. The results showed that it is difficult to train a classifier to be accurate over large datasets (15 subjects) but KNN and SVM with the proposed features were reasonably accurate over smaller datasets (5 subjects) identifying the emotional states with an accuracy up to 77.78%. | |||
| From Explicit to Implicit Speech Recognition | | BIBA | Full-Text | 502-511 | |
| Chad M. Spooner; Erik Viirre; Bradley Chase | |||
| We consider the problem of determining the word or concept that a subject holds in their mind prior to the act of speech using only a scalp-recorded electroencephalogram (EEG). Such speech acts are called covert, silent, or implicit speech acts in the literature. We consider a binary-tree classifier that uses one of a number of candidate feature types, including temporal correlation coefficients, spectral correlation, and time-gated raw voltages. The particular features and binary-tree parameters are blindly determined using the local discriminant basis (LDB) technique. The experiments involve sequential presentation of words and numbers on a computer screen. The subject wears an EEG scalp cap and is instructed to first consider the stimulus, then speak it. Later, the subject is instructed to perform the same task without the actual utterance, resulting in implicit speech. We present performance results for the various obtained classifiers, which show that the approach has significant merit. | |||
| Cognitive-Affective Interactions in Strategic Decision Making | | BIBAK | Full-Text | 512-520 | |
| Yanlong Sun; Hongbin Wang | |||
| While making a decision to maximize the expected utility is among the prime
examples of human intelligence, the ultimatum game showcases a social dilemma
where people sacrifice their economic self-interest in the presence of negative
emotions. In the present study, we explore human cognitive-affective
interactions in strategic thinking from an integrated neurocomputational
perspective. We manipulated participants' emotions by inducing incidental
affective states in the ultimatum game. We found that participants' rejection
rates of unfair offers were significantly lower in positive valence emotions
("happy" and "calm") than in negative valence emotions ("sad" and "anxious").
In addition, the reduction of rejection rates appeared to be independent of the
arousal level (high arousal in "happy" and "anxious" versus low arousal in
"calm" and "sad"). Our results suggested that positive valence emotions, by
broadening people's evaluations of decision perspectives and alleviating the
perception of unfairness, may help people regain focus on their economic
self-interest. Keywords: Decision making; social dilemma; ultimatum game; affective induction;
fairness preference; valence; arousal | |||
| Translation of EEG-Based Performance Prediction Models to Rapid Serial Visual Presentation Tasks | | BIBAK | Full-Text | 521-530 | |
| Jon Touryan; Gregory Apker; Scott Kerick; Brent Lance; Anthony J. Ries; Kaleb McDowell | |||
| Brain wave activity is known to correlate with decrements in behavior
brought on by fatigue, boredom or low levels of alertness. Being able to
predict these behavioral changes from the neural activity via
electroencephalography (EEG) is an area of ongoing interest. In this study we
used an established approach to predict time-on-task decrements in behavior for
both a realistic driving simulator and a difficult perceptual discrimination
task, utilized in many brain-computer interface applications. The goal was to
quantify how well EEG-based models of behavior, developed for a driving
paradigm, extend to this non-driving task. Similar to previous studies, we were
able to predict time-on-task behavioral effects from the EEG power spectrum for
a number of participants in both the driving and perception tasks. Keywords: EEG; Fatigue; Power Spectral Density; Driving; RSVP | |||
| Adult Neurogenesis: Implications on Human And Computational Decision Making | | BIBAK | Full-Text | 531-540 | |
| Craig M. Vineyard; Stephen J. Verzi; Thomas P. Caudell; Michael L. Bernard; James B. Aimone | |||
| Adult neurogenesis is the incorporation of new neurons into established,
functioning neural circuits. Current theoretical work in the neurogenesis field
has suggested that new neurons are of greatest importance in the encoding of
new memories, particularly in the ability to fully capture features which are
entirely novel or being experienced in a unique way. We present two models of
neurogenesis (a spiking, biologically realistic model as well as a basic
growing feedforward model) to investigate possible functional implications. We
use an information theoretic computational complexity measure to quantitatively
analyze the information content encoded with and without neurogenesis in our
spiking model. And neural encoding capacity (as a function of neuron
maturation) is examined in our simple feedforward network. Finally, we discuss
potential functional implications for neurogenesis in high risk environments. Keywords: Neurogenesis; Dentate Gyrus; Information Theoretic Complexity; Neural
Network Modeling | |||
| The Effects of Spatial Attention on Face Processing: An ERPs Study | | BIBAK | Full-Text | 541-550 | |
| Liang Zhang; Kan Zhang | |||
| Faces are one of the most biologically and socially significant objects in
the human environment, and therefore believed to be processed automatically. To
investigate whether the processing of faces is modulated by attention,
event-related potentials (ERPs) were recorded in response to the dynamic facial
stimuli. Spatial attention were manipulated by directing participants which
location to be attended. The results showed that face-sensitive component N170
was not influenced by spatial attention, suggesting that the processing of
faces was not modulated by the attention during the early stage. But the
late-latency components were influenced by spatial attention. It indicated that
the automatic processing of faces is more like to be partial rather than
complete. These findings on dynamically real face processing by ERPs are
expected to be used in the development of human-computer interactions. Keywords: Face processing; Spatial attention; ERPs | |||
| The Information Exoskeleton: Augmenting Human Interaction with Information Systems | | BIBAK | Full-Text | 553-561 | |
| James P. Allen; Susan Harkness Regli; Kathleen M. Stibler; Patrick Craven; Peter Gerken; Patrice D. Tremoulet | |||
| In the military intelligence cycle the warfighter acts as both a receiver
and a producer of information. As a receiver the warfighter must be able to
readily assimilate disparate mission-relevant information. As a producer the
warfighter must be cognizant of both the current information requirements and
the ability to meet them. Both of these tasks are exacerbated by the heat of
battle and, in the case of the receiver, the ever-increasing amount of
available information. To address these challenges Lockheed Martin Advanced
Technology Laboratories (LM ATL) is creating a suite of capabilities to augment
warfighter interaction with intelligence services. Much like a powered
exoskeleton augments human interaction with the physical environment, our
Information Exoskeleton augments the warfighter's interaction with
intelligence, providing greater situational awareness with minimal operational
overhead. This paper describes our vision for the Information Exoskeleton, the
capabilities required to realize it, and related research efforts. Keywords: Information Exoskeleton; Information Needs Assessment; Context Awareness;
Information Alignment; Cognitive Alignment | |||
| QEEG Biomarkers: Assessment and Selection of Special Operators, and Improving Individual Performance | | BIBAK | Full-Text | 562-571 | |
| Donald R. DuRousseau | |||
| Future military special operator selection and education programs will take
advantage of state-of-the-art neuroimaging and normative statistical tools in
the creation of a customized database of EEG patterns gathered from top
performing specialists over their careers. Such a quantitative EEG Normative
Database (qEND) will function as the benchmark for screening, assessment,
selection and even training of targeted individuals required to work
effectively as operators under extreme stresses and for extended periods. This
assumption implies that an improved warfighter selection and training pedagogy
will embrace the concept of a "model" brain activity pattern (BAP) that
represents a warfighter at peak potential and in a highly focused and resilient
state of mind. It also implies that this model BAP can be used to: 1) identify
biomarkers of positive traits in candidates for specialized training programs,
and 2) reduce stress and improve sleep and training performance of program
selectees using guided EEG neurofeedback to maintain an optimal BAP. One such
statistical qEND (NeuroGuide) is used clinically in the assessment and
diagnosis of EEG imbalances specifically related to neurological and behavioral
disorders, as well as for guiding individual brain pattern changes through the
use of neurofeedback training (NT).
To evaluate qEEG for monitoring an individual's BAP changes and potentially improving mood and work performance, two military specialists with leadership experience underwent a program of pre- and post-EEG recordings and 20 neurofeedback training (NT) sessions. Here, the NeuroGuide database was used to determine how each participant's BAP differed from the age-matched group norms, and it was also used during the NT process to inform the software of the differences from the norms at each of the 4 training sites used to adjust the trainees EEG towards the direction of "normal". Changes from the NT program were assessed pre- and post-intervention using seven neuropsychological assessments of mood, anxiety, sleep, work performance and life satisfaction. In addition, one subject had a series of blood draws taken over the course of the NT program to evaluate changes in his plasma Cortisol; a reliable biomarker of stress level. Both subjects reported reduced levels of anxiety, impulsivity and anger, and improved mood and life satisfaction after the 20-session NT intervention. Keywords: Assessment and Selection; Biomarkers; Quantitative EEG; Neurofeedback;
Normative Statistics; Training Technologies; Training Policy | |||
| Ecological Momentary Storytelling: Bringing Down Organizational Stress through Qualifying Work Life Stories | | BIBAK | Full-Text | 572-581 | |
| Lisbeth Højbjerg Kappelgaard; Katja Lund | |||
| The purpose of this article is to examine ways in which a combination of
ecological momentary assessments and reflective dialogues can provide a
methodological framework for qualifying work-life stories in the process of
reducing organizational stress. The article is based on two hypotheses: 1) a
general as well as a work-related sense of coherence can mobilize resistance to
stressors and 2) a sense of coherence can occur through self-reflective
narratives which clarify patterns of action for oneself and for others.
Focusing on hearing impaired people in the Danish work force as well as primary
school teachers, the authors create a stress tracking method based on
HRV-measurements coupled with mobile questionnaires and reflective dialogues.
Findings in the user-test indicated that the method is a tool that creates a
story-based foundation on which it is possible to start a process of talking
about own experiences, stress and stressors, strategies, contexts etc. when
dealing with organizational stress. Keywords: Ecological Momentary Assessments (EMA); organizational stress; Experience
Sampling Method (ESM); Heart Rate Variability (HRV); Sense of Coherence (SOC) | |||
| The Development and Application of a Novel Physiological Metric of Cognitive Workload | | BIBAK | Full-Text | 582-590 | |
| Jeremy C. Rietschel; Matthew W. Miller | |||
| An objective assessment of the cognitive burden imposed by a task (cognitive
workload) is of fundamental interest in that it would provide a "window" into
one's current allocation of cognitive resources. Such insight would have
tremendous implications in maximizing human performance through a multitude of
applications including human-computer interaction. The authors propose a novel,
electroencephalographic (EEG)-derived metric, which relies on the event-related
potential (ERP) component, novelty-P3. A theoretical rationale and experimental
evidence supporting the metric's utility are provided, followed by future
directions. Keywords: Cognitive workload; human performance; EEG; novelty-P3 | |||
| Controlling Attention in the Face of Threat: A Method for Quantifying Endogenous Attentional Control | | BIBAK | Full-Text | 591-598 | |
| Bartlett A. H. Russell; Bradley D. Hatfield | |||
| It is well established that anxiety causes attentional narrowing and
increases distractibility, yet metrics are lacking for measuring these
phenomena during performance. Attention Control Theory (ACT) postulates that
anxiety consumes limited executive resources that are necessary for maintaining
goal-oriented, "top-down" attentional control and for suppressing
stimulus-driven, "bottom-up" distraction. While previous work has quantified
the effect of anxious states and traits on bottom-up distraction, it is far
more difficult to measure endogenous top-down attention. Here we briefly review
theories and previous findings regarding anxiety's affect on attention control
and discuss an ongoing study examining sustained attention under neutral and
anxiogenic conditions. The study employs a combination of established
Electroencephalographic (EEG) methods that together may offer a way to measure
top-down sustained attention. If successful, the method could help build a more
complete theoretical picture of attention control, and provide a way for HCI
platforms to monitor user states in changing contexts. Keywords: Attention control; Steady State Visual Evoked Potentials; anxiety | |||
| Developing Visualization Techniques for Improved Information Comprehension and Reduced Cognitive Workload | | BIBAK | Full-Text | 599-607 | |
| Scott Scheff; Tristan Plank; John Wilson; Angelia Sebok | |||
| In today's data rich environments, enormous quantities of digital
information can now be collected and made available to end-users in a wide
variety of domains. With so much information now readily accessible, effective
display methods that integrate and make sense of the data are needed; otherwise
end-users may quickly become overwhelmed. HF Designworks, Inc. and Alion
Science & Technology have developed tools that leverage large quantities of
information to provide useful visualizations to the warfighter. This paper
describes the approach and results of two related projects, iWarrior and My
Heat Maps, where we provide end-users with deep data comprehension without
imposing cognitive overload. Keywords: Applications of Augmented Cognition | |||
| Development of Fatigue-Associated Measurement to Determine Fitness for Duty and Monitor Driving Performance | | BIBAK | Full-Text | 608-617 | |
| Ying Ying Tan; Sheng Tong Lin; Frederick Tey | |||
| Long distance driving has been a major factor leading to road accidents
[1-2]. With the lack of reliable validation on driver fatigue technology
systems [3], the aim of this study is to correlate the measurements of two
cognitive tests: Psychomotor Vigilance Task Tester-PVT [4] and PenScreen-PS [5]
to establish the threshold levels of fatigued driving performance that will
form the basis to prevent fatigued drivers from handling vehicles. PVT is
recommended to be the first line of defense against putting fatigued drivers on
duty. Drowsiness can be detected by SmartEye Anti-Sleep-AS, acting as a
monitoring tool. Eye closure analysis on AS's eyelid opening data showed that
AS is a feasible system for real-time monitoring of fatigue while driving. The
results also suggested a simpler and more economical way of monitoring fatigue
using AS system. PS could be used in conjunction with PVT to detect for any
malingering intent. Keywords: Fatigue; Fitness for Duty; Driving Performance | |||
| Novel Tools for Driving Fatigue Prediction: (1) Dry Eeg Sensor and (2) Eye Tracker | | BIBAK | Full-Text | 618-627 | |
| Frederick Tey; Sheng Tong Lin; Ying Ying Tan; Xiao Ping Li; Andrea Phillipou; Larry Abel | |||
| National Sleep Foundation's Sleep in America (2005) reported 60% of adult
drivers driving a vehicle while feeling drowsy in the past year, and more than
37% have actually fallen asleep at the wheel [1]. This paper presented the
findings of two novel fatigue prediction tools. The first study presents a
4-channel dry EEG under simulated driving being able to predict when the driver
will develop microsleep in the next 10 minutes using only 3 minutes data of
collected, with an accuracy of more than 80%. The second study uses an eye
tracker to assess the percentage of time that the eyelids were closed (PERCLOS)
as a potential marker for fatigue. Results showed that the average magnitude of
oscillation (amount of pupil fluctuation), known as Coefficient Magnitude (CM),
is generated from real-time wavelet analysis, has the potential to predict
fatigue 8-12 minutes ahead with 84% accuracy ahead of compromised driving
behavior. Keywords: Fatigue; dry EEG; eye tracker; microsleep | |||
| Quantifying Resilience to Enhance Individualized Training | | BIBAK | Full-Text | 628-636 | |
| Brent Winslow; Meredith Carroll; David Jones; Frank Hannigan; Kelly Hale; Kay Stanney; Peter Squire | |||
| Resilience is the human ability to adapt in the face of tragedy, trauma,
adversity, hardship, and ongoing life stressors. To date, experimental reports
on this subject have focused on long-term trajectories (weeks to months) of
resilience, with little or no focus on whether significant changes to
resilience could be achieved by short-term interventions. Currently, an
individual's resilience is defined either by self-report or by behavioral
changes such as the development of depression, post-traumatic stress disorder,
or suicide. We propose that the quantification of an individual's physiological
and behavioral response to stress under controlled conditions is an indication
of the individual's level of resilience. To address such real-time resilience,
we propose the first in a series of studies to evaluate real-time human
resilience by exposing participants to controlled stressors while assessing the
stress response. Activation of the hypothalamus-pituitary-adrenal cortex axis
and sympathetic branch of the autonomic nervous system via monitoring of the
pupil constriction, heart and respiration rate, muscle tonicity, salivary
cortisol, and electrodermal activity will be assessed. Stress exposure will
consist of virtual stressors presented using Virtual Battlespace 2
software-based scenarios, such as noise exposure, time pressure, and
emotion-induction tasks, as well as external stressors such as socio-evaluative
stress via the Trier social stress task, while evaluating decision-making and
performance. The relationship between performance and the physiological stress
response will be quantified, including the creation of a series of
stress-performance trajectories based upon individual differences. Such an
analysis is similar to probing for resilience in material testing, in which a
load is applied to a candidate material, and the resulting forces and
observable changes in dimension are quantified and reported via stress-strain
curves. Ongoing studies will examine how this resilience measure may be
integrated into a closed-loop training system to provide appropriate coping
strategies to optimize resilience training. Such training programs, which take
into account individual perceptions of stressors and physiological responses,
are expected to be effective in helping trainees develop resiliency during
high-stress operations. Keywords: Resilience; Stress; Adaptation; Training; Autonomic Nervous System | |||
| So Fun It Hurts -- Gamifying an Engineering Course | | BIBAK | Full-Text | 639-648 | |
| Gabriel Barata; Sandra Gama; Joaquim Jorge; Daniel Gonçalves | |||
| Good games are good motivators by nature, as they make players feel rewarded
and fulfilled, which pushes them forward to persist and resist frustration.
Gamification is a novel technique that uses game elements like points and
badges, to motivated and engage users into embracing new behaviors, such as
improving one's health condition, finances or productivity. In this paper, we
present an experiment in which an MSc college course was gamified to improve
student interest and engagement. The gamified course led to better learning
results and participation. However, there were several negative side effects
that detracted from the overall experience. We will describe them, identifying
their causes and describe possible alternatives to better tailor the gamified
experience, stemming from the analysis of the data gathered so far. Keywords: Education Gamification; Perils; Student engagement; Motivation | |||
| A Practical Mobile Dry EEG System for Human Computer Interfaces | | BIBA | Full-Text | 649-655 | |
| Yu M. Chi; Yijun Wang; Yu-Te Wang; Tzyy-Ping Jung; Trevor Kerth; Yuchen Cao | |||
| A complete mobile electroencephalogram (EEG) system based on a novel, flexible dry electrode is presented. The wireless device features 32-channels in a soft, adjustable headset. Integrated electronics enable high resolution (24-bit, 250 samples/sec) acquisition electronics and can acquire operate for more than four hours on a single AAA battery. The system weighs only 140 g and is specifically optimized for ease of use. After training users can self-don the headset in around three minutes. Test data on multiple subjects with simultaneously acquired EEGs from a traditional wet, wired system show a very high degree of signal correlation in AEP and P300 tasks. | |||
| Gamification for Measuring Cyber Security Situational Awareness | | BIBAK | Full-Text | 656-665 | |
| Glenn Fink; Daniel Best; David Manz; Viatcheslav Popovsky; Barbara Endicott-Popovsky | |||
| Cyber defense competitions arising from U.S. service academy exercises,
offer a platform for collecting data that can inform research that ranges from
characterizing the ideal cyber warrior to describing behaviors during certain
challenging cyber defense situations. This knowledge could lead to better
preparation of cyber defenders in both military and civilian settings. This
paper describes how one regional competition, the PRCCDC, a participant in the
national CCDC program, conducted proof of concept experimentation to collect
data during the annual competition for later analysis. The intent is to create
an ongoing research agenda that expands on this current work and incorporates
augmented cognition and gamification methods for measuring cybersecurity
situational awareness under the stress of cyber attack. Keywords: Cyber Defense Competitions; CCDC; cyber defender; cyberwarrior | |||
| Human-Robotic Collaborative Intelligent Control for Reaching Performance | | BIBAK | Full-Text | 666-675 | |
| Rodolphe J. Gentili; Hyuk Oh; Isabelle M. Shuggi; Ronald N. Goodman; Jeremy C. Rietschel; Bradley D. Hatfield; James A. Reggia | |||
| In most human-robot interfaces, the user completely controls the robot that
operates as a passive tool without adaptation capabilities. However, a
synergetic human-robot interface where both agents collaborate could improve
the user's performance while reducing the cognitive and physical workload.
Specifically, when considering this framework applied to rehabilitation, we
examined a shared collaborative control between a human user and an adaptive
biologically inspired neurocontroller in order to perform reaching movements
with a simulated prosthetic arm. When this neurocontroller was enabled, it
progressively learned from the user to control the prosthetic arm, increasing
its role in the shared performance and facilitating the user's reaching
movements. This resulted in the user's performance enhancement and in a
reduction of his/her cognitive workload. The long term goal of this work is to
contribute to the development of the next generation of intelligent
human-robotic interfaces for rehabilitation. Keywords: Human-machine/robot collaborative performance; intelligent control; adaptive
systems; arm reaching; assistive technology; prosthetic arm; rehabilitation | |||
| Combining Augmented Cognition and Gamification | | BIBAK | Full-Text | 676-684 | |
| Curtis S. Ikehara; Martha E. Crosby; Paula Alexandra Silva | |||
| The strategic goal of augmented cognition is to increase task performance
capacity by using physiological sensor feedback to adjust or modify the
activity for the user. Gamification has been shown to increase performance by
using certain combinations of game elements. Both augmented cognition and
gamification address increased task performance capacity. Gamification adds to
augmented cognition by directly addressing the motivation of the user to remain
engaged in the activity. This has also been referred to as flow, or the optimal
experience. This paper describes an example of a gamified activity in which the
physiological sensors of augmented cognition are used to foster the optimal
experience desired in gamification. Also, discussed is how the strategic goals
of augmented cognition and gamification overlap through the use of a gamified
example that describes how the components of augmented cognition and elements
of gamification can be used together to better achieve the goal of increased
task performance capacity. Keywords: augmented cognition; gamification; physiological sensors | |||
| Issues in Implementing Augmented Cognition and Gamification on a Mobile Platform | | BIBAK | Full-Text | 685-694 | |
| Curtis S. Ikehara; Jiecai He; Martha E. Crosby | |||
| There are two major trends in computing that will impact augmented
cognition. The first is the shift in computing platform from the desktop to
mobile computing (e.g., smartphone and tablet) because the user wants to be
able to do computing tasks where ever they are. The second trend is the
gamification of computer applications to keep the user engaged and motivated.
Compared to a workstation, the mobile computing environment is a challenge
because of limited computing power, storage capacity, internet connectivity and
battery capacity. This paper discusses the issues involved in implementing
augmented cognition activities on a mobile platform and the tradeoffs of
gamifying augmented cognition activities. These issues are discussed in terms
of two example mobile platform applications that implement internal and
external sensors. Keywords: mobile computing; augmented cognition; gamification; physiological sensors | |||
| Visual Analysis and Filtering to Augment Cognition | | BIBAK | Full-Text | 695-702 | |
| Mathias Kölsch; Juan Wachs; Amela Sadagic | |||
| We built and demonstrated a system that augments instructors' sensing
abilities and augments their cognition through analysis and filtering of visual
information. Called BASE-IT, our system helps US Marine instructors provide
excellent training despite the challenging environment, hundreds of trainees
and high trainee-to-instructor ratios, non-stop action, and diverse training
objectives. To accomplish these objectives, BASE-IT widens the sensory input in
multiple dimensions and filters relevant information: BASE-IT a) establishes
omnipresence in a large training area, b) supplies continuous evaluation during
multi-day training, c) pays specific attention to every individual, d) is
specially equipped to identify dangerous situations, and e) maintains virtual
vantage points for improved situational awareness. BASE-IT also augments and
personalizes the after-action review information available to trainees.
This paper focuses on the automated data analysis component, how it supplements the information available to instructors, and how it facilitates understanding of individual and team performances on the training range. Keywords: Augmented cognition; information analysis; training range instrumentation | |||
| A Novel HCI System Based on Real-Time fMRI Using Motor Imagery Interaction | | BIBAK | Full-Text | 703-708 | |
| Xiaofei Li; Lele Xu; Li Yao; Xiaojie Zhao | |||
| Real-time functional resonance imaging (rtfMRI) provides an emerging
human-computer interaction (HCI) technology with relatively high spatial
resolution. The motor imagery is widely used for sports training of athletes
and motor ability rehabilitation of patients, which is a common interaction
approach for EEG-based and fMRI-based BCI. An appropriate method of interaction
can improve the performance of BCI. In this paper, we implemented a novel HCI
system based on rtfMRI using motor imagery interaction. The user interacted
with the system by regulating blood oxygenation level dependent (BOLD) signal
intensity of the region of interest (ROI) in motor areas using motor imagery,
which was presented by the running speed of a virtual human in an animation.
The ROI was chosen according to the motor network resulted from the real-time
independent component analysis (rtICA). Through the interaction with the HCI
system, the user could learn the effectiveness of his motor imagery. Keywords: HCI system; real-time fMRI; motor imagery; animation interaction | |||
| Guided Learning Algorithms: An Application of Constrained Spectral Partitioning to Functional Magnetic Resonance Imaging (fMRI) | | BIBAK | Full-Text | 709-716 | |
| Henry L. Phillips; Peter B. Walker; Carrie H. Kennedy; Owen Carmichael; Ian N. Davidson | |||
| Innovations in neuro-technology have created a potential gap in our ability
to measure human performance and decision making in dynamic environments.
Therefore, a need exists to create more reliable testing methodologies and data
analytic solutions. The primary aim of this paper is to describe work to
integrate subject matter expertise with algorithms designed to measure human
brain activity in real time. Specifically, Guided Learning using constrained
spectral partitioning to increase the reliability and interpretability of fMRI
data is explicated and applied as a test case to the Default Mode Network in
the elderly population. How Guided Learning can be further applied to other
neuro-imaging technologies that may be more conducing to furthering the field
of augmented cognition is discussed. Keywords: augmented cognition; functional connectivity; fMRI | |||
| Next Generation of Physical Training Environments: Bringing in Sensor Systems and Virtual Reality Technologies | | BIBAK | Full-Text | 717-726 | |
| Amela Sadagic | |||
| Training on physical training ranges is immensely important to any military
unit, as many aspects of individual and team skills still need to be trained
there. Nevertheless, the overall cost of training on physical ranges, the
required unit throughput, as well as a need to maximize the training potential
that such precious environments have, are ever increasing, and leveraging
emerging technologies to make the training more effective becomes a necessity.
This paper reviews several novel efforts in the research domain that could be
used as a guide to the types of emerging technical solutions that may be
employed to augment the capabilities of physical training ranges, including the
capabilities of humans engaged in orchestrating and executing the training
events (range operators and instructors), and to support the global objective
of acquiring more effective training solutions. Keywords: physical training ranges; sensor systems; virtual reality (VR); augmented
reality (AR); automated behavior analysis; performance evaluation | |||
| A Study on Application of RB-ARQ Considering Probability of Occurrence and Transition Probability for P300 Speller | | BIBA | Full-Text | 727-733 | |
| Eri Samizo; Tomohiro Yoshikawa; Takeshi Furuhashi | |||
| Brain-Computer Interfaces (BCIs) control a computer or a machine based on the information of the signal of human's brain. P300 speller is one of the BCI communication tools, which uses P300 as the feature quantity and allows users to select letters just by thinking. Because of the low signal-to-noise ratio of the P300, signal averaging is often performed to improve the spelling accuracy instead of the degradation of the spelling speed. In texts, there is variability in occurrence probabilities and transition probabilities between letters. This paper proposes P300 speller considering the occurrence probabilities and the transition probabilities as the prior probabilities in RB-ARQ. It shows that the spelling speed and then the Utility were improved by the proposed method comparing with the conventional method. | |||
| Improvement of Sensory Stabilization and Repeatability of Vibration Interface for Distance Presentation | | BIBA | Full-Text | 734-743 | |
| Yuki Sampei; Takayuki Tanaka; Yuki Mori; Shun'ichi Kaneko | |||
| We have developed a vibration alert interface (VAI) that provides information through various vibration patterns. In our previous studies, we designed the VAI and its vibration patterns to provide analog-like information to users such as distance to obstacles. Precise information recognition requires correct perception of vibration patterns. However, various disturbances can affect perception of vibrations, causing users to perceive similar vibrations as being different. We therefore proposed the relative vibration sense presentation method to avoid disruption of the vibration sense. In this paper, we experimentally show that this method improves the repeatability of vibration sensation. We also propose a vibration presentation model for drivers to correct perception gaps due to the application and surroundings of VAI. We evaluate the proposed model through experimentation. | |||
| Effect of Light Priming and Encouraging Feedback on the Behavioral and Neural Responses in a General Knowledge Task | | BIBAK | Full-Text | 744-753 | |
| Andreea Ioana Sburlea; Tsvetomira Tsoneva; Gary Garcia-Molina | |||
| The increase of cognitive demands in society nowadays requires new ways to
deal with problems, such as burnout and mental fatigue. Lately, more and more
scientifically-based rigorous research in the area of brain-computer interfaces
has been done in the quest for restoring and augmenting cognition. The current
research work investigates light-based priming and positive reinforcement as
possible mediators of cognitive enhancement. Keywords: priming with light; cognitive enhancement; positive feedback | |||
| Using the Smartphone Accelerometer to Monitor Fall Risk while Playing a Game: The Design and Usability Evaluation of Dance! Don't Fall | | BIBAK | Full-Text | 754-763 | |
| Paula Alexandra Silva; Francisco Nunes; Ana Vasconcelos; Maureen Kerwin; Ricardo Moutinho; Pedro Teixeira | |||
| Falls are dangerous, and unfortunately common for older adults. Dance! Don't
Fall is a game that assesses the quality of the user's locomotion based on data
from the accelerometer of a smartphone. By providing a form of exercise, the
game may actually reduce fall risk as well as monitoring it. In this paper, we
document the development of the prototype and a usability study with ten
seniors that suggested the game is well suited to its primary users. Keywords: Fall risk assessment; older adults; mobile applications; physical activity;
dance games | |||
| Augmented Interaction: Applying the Principles of Augmented Cognition to Human-Technology and Human-Human Interactions | | BIBAK | Full-Text | 764-773 | |
| Anna Skinner; Lindsay Long; Jack Vice; John Blitch; Cali M. Fidopiastis; Chris Berka | |||
| The field of Augmented Cognition (AugCog) has evolved over the past decade
from its origins in the Defense Advanced Research Projects Agency
(DARPA)-funded research program, emphasizing modulation of closed-loop
human-computer interactions within operational environments, to address a
broader scope of domains, contexts, and science and technology (S&T)
challenges. Among these are challenges related to the underlying theoretical
and empirical research questions, as well as the application of advances in the
field within contexts such as training and education. This paper summarizes a
series of ongoing research and development (R&D) efforts aimed at applying
an AugCog-inspired framework to enhance both human-technology and human-human
interactions within a variety of training and operational domains. Keywords: Augmented Cognition; Training; Simulation; Human-Robot Interaction; Adaptive
Automation; Neuroscience; Psychophysiological Measures; EEG | |||
| Integration of Automated Neural Processing into an Army-Relevant Multitasking Simulation Environment | | BIBAK | Full-Text | 774-782 | |
| Jon Touryan; Anthony J. Ries; Paul Weber; Laurie Gibson | |||
| Brain-computer interface technology has experienced a rapid evolution over
recent years. Recent studies have demonstrated the feasibility of detecting the
presence or absence of targets in visual imagery from the neural response
alone. Classification accuracy persists even when the imagery is presented
rapidly. While this capability offers significant promise for applications that
require humans to process large volumes of imagery, it remains unclear how well
this approach will translate to more real-world scenarios. To explore the
viability of automated neural processing in an Army-relevant operational
context, we designed and built a simulation environment based on a ground
vehicle crewstation. Here, we describe the process of integrating and testing
the automated neural processing capability within this simulation environment.
Our results indicate the potential for significant benefits to be realized by
incorporating brain-computer interface technology into future Army systems. Keywords: Simulator; Brain-Computer Interface (BCI); Visual Search | |||
| Behavioral Biometric Identification on Mobile Devices | | BIBAK | Full-Text | 783-791 | |
| Matt Wolff | |||
| We show that accelerometers, touch screens and software keyboards, which are
standard components of modern mobile phones, can be used to differentiate
different test subjects based on the unique interaction characteristics of each
subject. This differentiation ability can be applied to authenticate
individuals under a continuous authentication scheme. Based on six 15 minute
data sets collected from the test subjects utilizing our data collection
platform, we extract multiple features from the data and show an ability to
accurately identify individuals at a rate of 83 percent using a simple normal
distribution of each feature. Keywords: identification; security | |||