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2023 |
Kamti, Mukesh Kumar; Iqbal, Rauf; Kakoti, Pallabjyoti Eeg-based mental states assessment of three-wheeler drivers in different environments and traffic conditions Journal Article In: Transportation Research Part F: Traffic Psychology and Behaviour, vol. 99, pp. 98–112, 2023. Abstract | Links | Tags: Cognitive-Algorithm, DSI-7 @article{kamti2023eeg,Driving in diverse and challenging conditions, including inclement weather, poses potential risks to road safety. While previous studies have primarily focused on examining driver behavior and reactions in different weather and road conditions, there is a lack of research on assessing drivers' mental states during such situations, particularly considering the influence of factors such as road complexity, traffic, demographics, and adverse environmental conditions. This paper aims to address this research gap by evaluating the mental states of drivers across different age groups and driving experience levels through simulated driving scenarios encompassing various environments and traffic conditions. A three-wheeler driving simulator was employed, along with the DSI 7 EEG headset and Q states software, to classify and analyze the drivers' mental states. The findings of this study highlight that young novice drivers exhibit higher fluctuations in mental state compared to their mid and high-experienced counterparts. Furthermore, mid-age drivers face an elevated risk of collision due to frequent changes in mental state and attention. Additionally, it was observed that highly skilled drivers display a transition in attention level and mental state between sessions, shifting from a focused to a relaxed state—an aspect absent in inexperienced drivers. These findings enhance our comprehension of the intricate interaction among drivers' emotional states, age, experience, and driving abilities, consequently opening avenues for tailored interventions and training initiatives focused on improving road safety. |
2021 |
Dong, Sunghee; Jin, Yan; Bak, SuJin; Yoon, Bumchul; Jeong, Jichai Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity Journal Article In: Electronics, vol. 10, no. 23, pp. 3020, 2021. Abstract | Links | Tags: Cognitive-Algorithm, DSI-24 @article{dong2021explainable,Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by the current machine learning techniques because of a lack of physiological understanding. To investigate the suitability of FC in BCIs for the elderly, we propose the decoding of lower- and higher-order FC using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. A total of 17 young adults (24.5±2.7 years) and 12 older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. The CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increased the classification accuracy by 88.3% compared to the filter-bank common spatial pattern. The LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe, depending on the task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly |
Seo, Ssang-Hee Derivation of EEG Spectrum-based Feature Parameters for Mental Fatigue Determination Journal Article In: Journal of Convergence for Information Technology, vol. 11, no. 10, pp. 10–19, 2021. Abstract | Links | Tags: Cognitive-Algorithm, DSI-24 @article{seo2021derivation,In this paper, we tried to derive characteristic parameters that reflect mental fatigue through EEG measurement and analysis. For this purpose, mental fatigue was induced through a resting state with eyes closed and performing subtraction operations in mental arithmetic for 30 minutes. Five subjects participated in the experiment, and all subjects were right-handed male students in university, with an average age of 25.5 years. Spectral analysis was performed on the EEG collected at the beginning and the end of the experiment to derive feature parameters reflecting mental fatigue. As a result of the analysis, the absolute power of the alpha band in the occipital lobe and the temporal lobe increased as the mental fatigue increased, while the relative power decreased. Also, the difference in power between resting state and task state showed that the relative power was larger than the absolute power. These results indicate that alpha relative power in the occipital lobe and temporal lobe is a feature parameter reflecting mental fatigue. The results of this study can be utilized as feature parameters for the development of an automated system for mental fatigue determination such as fatigue and drowsiness while driving. |
Chakravarty, Sumit; Xie, Ying; Le, Linh; Johnson, John; Hales, Michael Comparison Between Active and Passive Attention Using EEG Waves and Deep Neural Network Conference International Conference on Brain Informatics, vol. 12960, Springer 2021, ISBN: 978-3-030-86993-9. Abstract | Links | Tags: Cognitive-Algorithm, DSI-24 @conference{chakravarty2021comparison,A person’s state of attentiveness can be affected by various outside factors. Having energy, feeling tired, or even simply being distracted all play a role in someone’s level of attention. The task at hand can potentially affect the person’s attention or concentration level as well. In terms of students who take online courses, constantly watching lectures and conducting these courses solely online can cause lack of concentration or attention. Attention can be considered in two categories: passive or active. Conducting active and passive attention-based trials can reveal different states of attentiveness. This paper compares active and passive attention trial results of the two states, wide awake and tired. This has been done in order to uncover a difference in results between the two states. The data analyzed throughout this paper was collected from DSI 24 EEG equipment, and the generated EEG is processed through a 3D Convolutional Neural Network (CNN) to produce results. Three passive attention trials and three active attention trials were performed on seven subjects, while they were wide awake and when they were tired. The experiments on the preprocessed data results in accuracies as high as 81.78% for passive attention detection accuracy and 63.67% for active attention detection accuracy, which shown a clear ability to separate between the two attention categories. |
Snider, Dallas H; Linnville, Steven E; Phillips, Jeffrey B; Rice, Merrill G Predicting hypoxic hypoxia using machine learning and wearable sensors Journal Article In: Biomedical Signal Processing and Control, vol. 71, pp. 103110, 2021. Abstract | Links | Tags: Cognitive-Algorithm, DSI-7 @article{snider2022predicting,The capability of detecting symptoms of hypoxia (i.e., reduced oxygen) and other cognitive impairments in-flight with wearable sensors and machine learning based algorithms will benefit the aviation community by saving lives and preventing mishaps. In this study, knowledge discovery processes were implemented to build classification models to predict hypoxia from wearable, dry-EEG sensor data collected from 85 participants in a two-phase study. Over a 35-minute period and while wearing aviation flight masks which regulated their oxygen intake, participants would alternate between a 2-minute cognitive test on CogScreen Hypoxia Edition and a 3-minute simulated flying task on X-Plane 11, with the oxygen concentration reducing every 5 min following the simulated flight task. The decrease in oxygen each 5 min simulated an increase in altitude. Features extracted from the EEG waveforms were transformed using principal component analysis to reduce the dimensionality of the data. Naïve Bayes, decision tree, random forest, and neural network algorithms were utilized to classify the transformed brain wave data as either normal or hypoxic. The algorithms sensitivity ranged from 0.83 to 1.00 while the specificity ranged from 0.91 to 1.00. This study makes a step forward in developing a real-time, in-flight hypoxia detection system. |
2020 |
Neilson, Brittany N; Phillips, Jeffrey B; Snider, Dallas H; Drollinger, Sabrina M; Linnville, Steven E; Mayes, Ryan S A Data-Driven Approach to Aid in Understanding Brainwave Activity During Hypoxia Conference 2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID), IEEE IEEE, Miramar Beach, FL, USA, 2020, ISBN: 978-1-7281-5890-7. Abstract | Links | Tags: Biomarker, Cognitive-Algorithm, DSI-7 @conference{neilson2020data,Changes in brainwave activity have been associated with hypoxia, but the literature is inconsistent. Twenty-five participants were subjected to normobaric hypoxia while undergoing a variety of cognitive tasks. The detected differences in brain activity between normal and hypoxic conditions are presented. |
Wang, Jiahui; Antonenko, Pavlo; Keil, Andreas; Dawson, Kara Converging subjective and psychophysiological measures of cognitive load to study the effects of instructor-present video Journal Article In: Mind, Brain, and Education, vol. 14, no. 3, pp. 279–291, 2020. Abstract | Links | Tags: Cognitive-Algorithm, DSI-24 @article{wang2020converging,Many online videos feature an instructor on the screen to improve learners' engagement; however, the influence of this design on learners' cognitive load is underexplored. This study investigates the effects of instructor presence on learners' processing of information using both subjective and psychophysiological measures of cognitive load. Sixty university students watched a statistics instructional video either with or without instructor presence, while the spontaneous electrical activity of their brain was recorded using electroencephalography (EEG). At the conclusion of the video, they also self-reported overall load, intrinsic load, extraneous load, and germane load they experienced during the video. Learning from the video was assessed via tests of retention and transfer. Results suggested the instructor-present video improved learners' ability to transfer information and was associated with a lower self-reported intrinsic and extraneous load. Event-related changes in theta band activity also indicated lower cognitive load with instructor-present video. |
2019 |
Goethem, Sander Van; Adema, Kimberly; van Bergen, Britt; Viaene, Emilia; Wenborn, Eva; Verwulgen, Stijn A Test Setting to Compare Spatial Awareness on Paper and in Virtual Reality Using EEG Signals Conference International Conference on Applied Human Factors and Ergonomics, Springer 2019. Abstract | Links | Tags: BCI, Cognitive-Algorithm, DSI-7, VR @conference{van2019test,Spatial awareness and the ability to analyze spatial objects, manipulate them and assess the effect thereof, is a key competence for industrial designers. Skills are gradually built up throughout most educational design programs, starting with exercises on technical drawings and reconstruction or classification of spatial objects from isometric projections and CAD practice. The accuracy in which spatial assignments are conducted and the amount of effort required to fulfill them, highly depend on individual insight, interests and persistence. Thus each individual has its own struggles and learning curve to master the structure of spatial objects in aesthetic and functional design. Virtual reality (VR) is a promising tool to expose subjects to objects with complex spatial structure, and even manipulate and design spatial characteristics of such objects. The advantage of displaying spatial objects in VR, compared to representations by projecting them on a screen or paper, could be that subjects could more accurately assess spatial properties of and object and its full geometrical and/or mechanical complexity, when exposed to that object in VR. Immersive experience of spatial objects, could not only result in faster acquiring spatial insights, but also potentially with less effort. We propose that acquiring spatial insight in VR could leverage individual differences in skills and talents and that under this proposition VR can be used as a promising tool in design education. A first step in underpinning this hypothesis, is acquisition of cognitive workload that can be used and compared both in VR and in a classical teaching context. We use electroencephalography (EEG) to assess brain activity through wearable plug and play headset (Wearable Sensing-DSI 7). This equipment is combined with VR (Oculus). We use QStates classification software to compare brain waves when conducting spatial assessments on paper and in VR. This gives us a measure of cognitive workload, as a ratio of a resulting from subject records with a presumed ‘high’ workload. A total number of eight records of subjects were suited for comparison. No significant difference was found between EEG signals (paried t-test, p = 0.57). However the assessment of cognitive workload was successfully validated through a questionnaire. The method could be used to set up reliable constructs for learning techniques for spatial insights. |
2018 |
Camp, Marieke Van; Boeck, Muriel De; Verwulgen, Stijn; Bruyne, Guido De EEG Technology for UX Evaluation: A Multisensory Perspective Conference International Conference on Applied Human Factors and Ergonomics, vol. 775, Springer Advances in Intelligent Systems and Computing , 2018. Abstract | Links | Tags: Cognitive-Algorithm, DSI-24, Neuromarketing @conference{van2018eeg,Along with a growing interest in experience-driven design, interest in measuring user experience has progressively increased. This study explores the use of EEG for empirical UX evaluation. A first experimental test was conducted to measure and understand the effect of sensory stimuli on the user experience. A first experimental test was carried out with eight participants. A series of videos, eliciting positive and negative emotional responses, were presented to the participants. Subsequently, auditory stimuli were introduced and the effect on the user experience was evaluated using EEG measurements techniques and analysis software. After the tests the participants were questioned to verify whether the subjective results matched the objective measurements. |
Raj, Anil; Roberts, Brooke; Hollingshead, Kristy; McDonald, Neil; Poquette, Melissa; Soussou, Walid A Wearable Multisensory, Multiagent Approach for Detection and Mitigation of Acute Cognitive Strain Conference International Conference on Augmented Cognition, Springer 2018. Abstract | Links | Tags: Cognitive-Algorithm @conference{raj2018wearable,While operators performing tasks with high workload can increase task performance in response to limited increases in cognitive stress, chronic or rapidly accelerating stress can exceed the operator’s ability to compensate, generating acute cognitive strain (ACS). ACS represents a state wherein performance, situation awareness and cooperativity deteriorate markedly, leading to critical errors, mishaps or casualties. Nearly two decades of augmented cognition (AugCog) research has demonstrated the utility of psychophysiologic sensing and analysis for identification and tracking of changes in cognitive state and to modulate human machine interactions for improving system task performance. The proposed approach leveraged prior efforts to modulate cognitive stress using a multiagent approach to acquire and analyze multiple Psychophysiologic sensory channels, including changes in vocalizations, to create a reliable and non-intrusive Detector of Acute Cognitive Strain (DACS). The DACS system provides an integrated wearable multi-modal Research Sensor Suite (RSS) using the open-source Adaptive Multiagent Integration (AMI) architecture, that includes analysis agents for electroencephalograph (EEG), electromyography (EMG), video oculography (VOG), vocalization, and others to identify and correlate physiological signatures with cognitive stress and strain. An online AMI agent-based processing algorithm was developed and applied to audio communications to evaluate for changes in speaker vocalization fundamental frequency (F0) and cadence (utterances per minute). This paper describes initial phase results of aerospace mishap vocalization stress marker detection, a potential element of the proposed DACS system. DACS could use these markers to trigger adaptive automation agents that reduce task load and allow pilots to prevent or recover from ACS episodes. |
2017 |
Mills, Caitlin; Fridman, Igor; Soussou, Walid; Waghray, Disha; Olney, Andrew M; D'Mello, Sidney K Put your thinking cap on: detecting cognitive load using EEG during learning Conference Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 2017. Abstract | Links | Tags: Cognitive-Algorithm, DSI-24 @conference{mills2017put,Current learning technologies have no direct way to assess students' mental effort: are they in deep thought, struggling to overcome an impasse, or are they zoned out? To address this challenge, we propose the use of EEG-based cognitive load detectors during learning. Despite its potential, EEG has not yet been utilized as a way to optimize instructional strategies. We take an initial step towards this goal by assessing how experimentally manipulated (easy and difficult) sections of an intelligent tutoring system (ITS) influenced EEG-based estimates of students' cognitive load. We found a main effect of task difficulty on EEG-based cognitive load estimates, which were also correlated with learning performance. Our results show that EEG can be a viable source of data to model learners' mental states across a 90-minute session. |
2012 |
Soussou, Walid; Rooksby, Michael; Forty, Charles; Weatherhead, James; Marshall, Sandra EEG and eye-tracking based measures for enhanced training Conference 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE 2012, ISSN: 1557-170X. Abstract | Links | Tags: Cognitive-Algorithm, DSI-24, Eye-Tracking @conference{soussou2012eeg,This paper describes a project whose goal was to establish the feasibility of using unobtrusive cognitive assessment methodologies in order to optimize efficiency and expediency of training. QUASAR, EyeTracking, Inc. (ETI), and Safe Passage International (SPI), teamed to demonstrate correlation between EEG and eye-tracking based cognitive workload, performance assessment and subject expertise on XRay screening tasks. Results indicate significant correlation between cognitive workload metrics based on EEG and eye-tracking measurements recorded during a simulated baggage screening task and subject expertise and error rates in that same task. These results suggest that cognitive monitoring could be useful in improving training efficiency by enabling training paradigms that adapts to increasing expertise. |
2011 |
McDonald, Neil J; Soussou, Walid Quasar's qstates cognitive gauge performance in the cognitive state assessment competition 2011 Conference 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE IEEE, 2011, ISSN: 1557-170X. Abstract | Links | Tags: Cognitive-Algorithm @conference{mcdonald2011quasar,The Cognitive State Assessment Competition 2011 was organized by the U.S. Air Force Research Laboratory (AFRL) to compare the performance of real-time cognitive state classification software. This paper presents results for QUASAR's data classification module, QStates, which is a software package for real-time (and off-line) analysis of physiologic data collected during cognitive-specific tasks. The classifier's methodology can be generalized to any particular cognitive state; QStates identifies the most salient features extracted from EEG signals recorded during different cognitive states or loads. |
2010 |
Estepp, Justin R; Monnin, Jason W; Christensen, James C; Wilson, Glenn F Evaluation of a Dry Electrode System for Electroencephalography: Applications for Psychophysiological Cognitive Workload Assessment Journal Article In: vol. 54, no. 3, pp. 210–214, 2010. Abstract | Links | Tags: Cognitive-Algorithm, Comparisons, DSI-24 @article{estepp2010evaluation,Advances in state-of-the-art dry electrode technology have led to the development of a novel dry electrode system for electroencephalography (QUASAR, Inc.; San Diego, California, USA). While basic systems-level testing and comparison of this dry electrode system to conventional wet electrode systems has proved to be very favorable, very limited data has been collected that demonstrates the ability of QUASAR's dry electrode system to replicate results produced in more applied, dynamic testing environments that may be used for human factors applications. In this study, QUASAR's dry electrode headset was used in combination with traditional wet electrodes to determine the ability of the dry electrode system to accurately differentiate between varying levels of cognitive workload. Results show that the accuracy in cognitive workload assessment obtained with wet electrodes is comparable to that obtained with the dry electrodes. |
Fielder, James Electroencephalogram (EEG) Study of Learning Effects across Addition Problems Technical Report PEBL Technical Report Series 2010. Links | Tags: Cognitive-Algorithm, DSI-24 @techreport{fielder2010electroencephalogram, |
2009 |
Matthews, R; Turner, PJ; McDonald, NJ; Ermolaev, K; Manus, T Mc; Shelby, RA; Steindorf, M Physiological Sensor Suite Using Zero Preparation Hybrid Electrodes for Real Time Workload Classification Journal Article In: The International Test and Evaluation Association, vol. 30, pp. 13–17, 2009. Abstract | Links | Tags: Cognitive-Algorithm @article{matthews2009physiological,Quantum Applied Science and Research is working closely with the Aberdeen Test Center to develop an integrated system to monitor warfighter physiology. This need has been recognized by two recent major programs: the Defense Advanced Research Projects Agency’s Augmented Cognition program and the U.S. Army’s Warfighter Physiological Status Monitor program. However, these programs were limited by inadequate development of fully deployable noninvasive sensors and in the number of physiological variables they could simultaneously measure. Warfighters need to rapidly perceive, comprehend, and translate combat information into action. To aid them, robust gauges have been developed for classification of cognitive workload, engagement, and fatigue, which simplify complex physiological data into onedimensional parameters that can be used to identify a subject’s cognitive state during the varied tasks carried out in a training environment. This article describes the two main hardware modules that form part of an integrated Physiological Sensor Suite (PSS): a Physiological Status Monitor (PSM) and a module for the measurement of electroencephalograms (EEGs). The PSS is based on revolutionary noninvasive bioelectric sensor technologies. No modification of the skin’s outer layer is required for the operation of this sensor technology, unlike conventional electrode technology that requires the use of conductive pastes or gels, often with abrasive skin preparation of the electrode site. The PSS was designed to be wearable and unobtrusive, with an emphasis on the capability of long-term monitoring of physiological signals. These factors are of considerable importance in operational settings where high end-user compliance is required. The PSM is a simple belt that is worn around the chest. The EEG system has already been incorporated into a soldier’s Kevlar helmet and tested successfully during combat training. Data are acquired using a miniature, ultralowpower, microprocessor-controlled multichannel data acquisition (DAQ) unit that transmits data wirelessly to a base station/data logger worn by the subject. The DAQ unit is worn on the body close to the measurement point, reducing the amount of cable clutter and minimizing the impact on subject mobility without introducing motion artifacts. |
2008 |
Matthews, R; Turner, PJ; McDonald, NJ; Ermolaev, K; Manus, T Mc; Shelby, RA; Steindorf, M Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes Conference 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE 2008. Abstract | Links | Tags: Cognitive-Algorithm @conference{matthews2008real,This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload |
2023 |
Kamti, Mukesh Kumar; Iqbal, Rauf; Kakoti, Pallabjyoti Eeg-based mental states assessment of three-wheeler drivers in different environments and traffic conditions Journal Article In: Transportation Research Part F: Traffic Psychology and Behaviour, vol. 99, pp. 98–112, 2023. @article{kamti2023eeg,Driving in diverse and challenging conditions, including inclement weather, poses potential risks to road safety. While previous studies have primarily focused on examining driver behavior and reactions in different weather and road conditions, there is a lack of research on assessing drivers' mental states during such situations, particularly considering the influence of factors such as road complexity, traffic, demographics, and adverse environmental conditions. This paper aims to address this research gap by evaluating the mental states of drivers across different age groups and driving experience levels through simulated driving scenarios encompassing various environments and traffic conditions. A three-wheeler driving simulator was employed, along with the DSI 7 EEG headset and Q states software, to classify and analyze the drivers' mental states. The findings of this study highlight that young novice drivers exhibit higher fluctuations in mental state compared to their mid and high-experienced counterparts. Furthermore, mid-age drivers face an elevated risk of collision due to frequent changes in mental state and attention. Additionally, it was observed that highly skilled drivers display a transition in attention level and mental state between sessions, shifting from a focused to a relaxed state—an aspect absent in inexperienced drivers. These findings enhance our comprehension of the intricate interaction among drivers' emotional states, age, experience, and driving abilities, consequently opening avenues for tailored interventions and training initiatives focused on improving road safety. |
2021 |
Dong, Sunghee; Jin, Yan; Bak, SuJin; Yoon, Bumchul; Jeong, Jichai Explainable Convolutional Neural Network to Investigate Age-Related Changes in Multi-Order Functional Connectivity Journal Article In: Electronics, vol. 10, no. 23, pp. 3020, 2021. @article{dong2021explainable,Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by the current machine learning techniques because of a lack of physiological understanding. To investigate the suitability of FC in BCIs for the elderly, we propose the decoding of lower- and higher-order FC using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. A total of 17 young adults (24.5±2.7 years) and 12 older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. The CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increased the classification accuracy by 88.3% compared to the filter-bank common spatial pattern. The LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe, depending on the task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly |
Seo, Ssang-Hee Derivation of EEG Spectrum-based Feature Parameters for Mental Fatigue Determination Journal Article In: Journal of Convergence for Information Technology, vol. 11, no. 10, pp. 10–19, 2021. @article{seo2021derivation,In this paper, we tried to derive characteristic parameters that reflect mental fatigue through EEG measurement and analysis. For this purpose, mental fatigue was induced through a resting state with eyes closed and performing subtraction operations in mental arithmetic for 30 minutes. Five subjects participated in the experiment, and all subjects were right-handed male students in university, with an average age of 25.5 years. Spectral analysis was performed on the EEG collected at the beginning and the end of the experiment to derive feature parameters reflecting mental fatigue. As a result of the analysis, the absolute power of the alpha band in the occipital lobe and the temporal lobe increased as the mental fatigue increased, while the relative power decreased. Also, the difference in power between resting state and task state showed that the relative power was larger than the absolute power. These results indicate that alpha relative power in the occipital lobe and temporal lobe is a feature parameter reflecting mental fatigue. The results of this study can be utilized as feature parameters for the development of an automated system for mental fatigue determination such as fatigue and drowsiness while driving. |
Chakravarty, Sumit; Xie, Ying; Le, Linh; Johnson, John; Hales, Michael Comparison Between Active and Passive Attention Using EEG Waves and Deep Neural Network Conference International Conference on Brain Informatics, vol. 12960, Springer 2021, ISBN: 978-3-030-86993-9. @conference{chakravarty2021comparison,A person’s state of attentiveness can be affected by various outside factors. Having energy, feeling tired, or even simply being distracted all play a role in someone’s level of attention. The task at hand can potentially affect the person’s attention or concentration level as well. In terms of students who take online courses, constantly watching lectures and conducting these courses solely online can cause lack of concentration or attention. Attention can be considered in two categories: passive or active. Conducting active and passive attention-based trials can reveal different states of attentiveness. This paper compares active and passive attention trial results of the two states, wide awake and tired. This has been done in order to uncover a difference in results between the two states. The data analyzed throughout this paper was collected from DSI 24 EEG equipment, and the generated EEG is processed through a 3D Convolutional Neural Network (CNN) to produce results. Three passive attention trials and three active attention trials were performed on seven subjects, while they were wide awake and when they were tired. The experiments on the preprocessed data results in accuracies as high as 81.78% for passive attention detection accuracy and 63.67% for active attention detection accuracy, which shown a clear ability to separate between the two attention categories. |
Snider, Dallas H; Linnville, Steven E; Phillips, Jeffrey B; Rice, Merrill G Predicting hypoxic hypoxia using machine learning and wearable sensors Journal Article In: Biomedical Signal Processing and Control, vol. 71, pp. 103110, 2021. @article{snider2022predicting,The capability of detecting symptoms of hypoxia (i.e., reduced oxygen) and other cognitive impairments in-flight with wearable sensors and machine learning based algorithms will benefit the aviation community by saving lives and preventing mishaps. In this study, knowledge discovery processes were implemented to build classification models to predict hypoxia from wearable, dry-EEG sensor data collected from 85 participants in a two-phase study. Over a 35-minute period and while wearing aviation flight masks which regulated their oxygen intake, participants would alternate between a 2-minute cognitive test on CogScreen Hypoxia Edition and a 3-minute simulated flying task on X-Plane 11, with the oxygen concentration reducing every 5 min following the simulated flight task. The decrease in oxygen each 5 min simulated an increase in altitude. Features extracted from the EEG waveforms were transformed using principal component analysis to reduce the dimensionality of the data. Naïve Bayes, decision tree, random forest, and neural network algorithms were utilized to classify the transformed brain wave data as either normal or hypoxic. The algorithms sensitivity ranged from 0.83 to 1.00 while the specificity ranged from 0.91 to 1.00. This study makes a step forward in developing a real-time, in-flight hypoxia detection system. |
2020 |
Neilson, Brittany N; Phillips, Jeffrey B; Snider, Dallas H; Drollinger, Sabrina M; Linnville, Steven E; Mayes, Ryan S A Data-Driven Approach to Aid in Understanding Brainwave Activity During Hypoxia Conference 2020 IEEE Research and Applications of Photonics in Defense Conference (RAPID), IEEE IEEE, Miramar Beach, FL, USA, 2020, ISBN: 978-1-7281-5890-7. @conference{neilson2020data,Changes in brainwave activity have been associated with hypoxia, but the literature is inconsistent. Twenty-five participants were subjected to normobaric hypoxia while undergoing a variety of cognitive tasks. The detected differences in brain activity between normal and hypoxic conditions are presented. |
Wang, Jiahui; Antonenko, Pavlo; Keil, Andreas; Dawson, Kara Converging subjective and psychophysiological measures of cognitive load to study the effects of instructor-present video Journal Article In: Mind, Brain, and Education, vol. 14, no. 3, pp. 279–291, 2020. @article{wang2020converging,Many online videos feature an instructor on the screen to improve learners' engagement; however, the influence of this design on learners' cognitive load is underexplored. This study investigates the effects of instructor presence on learners' processing of information using both subjective and psychophysiological measures of cognitive load. Sixty university students watched a statistics instructional video either with or without instructor presence, while the spontaneous electrical activity of their brain was recorded using electroencephalography (EEG). At the conclusion of the video, they also self-reported overall load, intrinsic load, extraneous load, and germane load they experienced during the video. Learning from the video was assessed via tests of retention and transfer. Results suggested the instructor-present video improved learners' ability to transfer information and was associated with a lower self-reported intrinsic and extraneous load. Event-related changes in theta band activity also indicated lower cognitive load with instructor-present video. |
2019 |
Goethem, Sander Van; Adema, Kimberly; van Bergen, Britt; Viaene, Emilia; Wenborn, Eva; Verwulgen, Stijn A Test Setting to Compare Spatial Awareness on Paper and in Virtual Reality Using EEG Signals Conference International Conference on Applied Human Factors and Ergonomics, Springer 2019. @conference{van2019test,Spatial awareness and the ability to analyze spatial objects, manipulate them and assess the effect thereof, is a key competence for industrial designers. Skills are gradually built up throughout most educational design programs, starting with exercises on technical drawings and reconstruction or classification of spatial objects from isometric projections and CAD practice. The accuracy in which spatial assignments are conducted and the amount of effort required to fulfill them, highly depend on individual insight, interests and persistence. Thus each individual has its own struggles and learning curve to master the structure of spatial objects in aesthetic and functional design. Virtual reality (VR) is a promising tool to expose subjects to objects with complex spatial structure, and even manipulate and design spatial characteristics of such objects. The advantage of displaying spatial objects in VR, compared to representations by projecting them on a screen or paper, could be that subjects could more accurately assess spatial properties of and object and its full geometrical and/or mechanical complexity, when exposed to that object in VR. Immersive experience of spatial objects, could not only result in faster acquiring spatial insights, but also potentially with less effort. We propose that acquiring spatial insight in VR could leverage individual differences in skills and talents and that under this proposition VR can be used as a promising tool in design education. A first step in underpinning this hypothesis, is acquisition of cognitive workload that can be used and compared both in VR and in a classical teaching context. We use electroencephalography (EEG) to assess brain activity through wearable plug and play headset (Wearable Sensing-DSI 7). This equipment is combined with VR (Oculus). We use QStates classification software to compare brain waves when conducting spatial assessments on paper and in VR. This gives us a measure of cognitive workload, as a ratio of a resulting from subject records with a presumed ‘high’ workload. A total number of eight records of subjects were suited for comparison. No significant difference was found between EEG signals (paried t-test, p = 0.57). However the assessment of cognitive workload was successfully validated through a questionnaire. The method could be used to set up reliable constructs for learning techniques for spatial insights. |
2018 |
Camp, Marieke Van; Boeck, Muriel De; Verwulgen, Stijn; Bruyne, Guido De EEG Technology for UX Evaluation: A Multisensory Perspective Conference International Conference on Applied Human Factors and Ergonomics, vol. 775, Springer Advances in Intelligent Systems and Computing , 2018. @conference{van2018eeg,Along with a growing interest in experience-driven design, interest in measuring user experience has progressively increased. This study explores the use of EEG for empirical UX evaluation. A first experimental test was conducted to measure and understand the effect of sensory stimuli on the user experience. A first experimental test was carried out with eight participants. A series of videos, eliciting positive and negative emotional responses, were presented to the participants. Subsequently, auditory stimuli were introduced and the effect on the user experience was evaluated using EEG measurements techniques and analysis software. After the tests the participants were questioned to verify whether the subjective results matched the objective measurements. |
Raj, Anil; Roberts, Brooke; Hollingshead, Kristy; McDonald, Neil; Poquette, Melissa; Soussou, Walid A Wearable Multisensory, Multiagent Approach for Detection and Mitigation of Acute Cognitive Strain Conference International Conference on Augmented Cognition, Springer 2018. @conference{raj2018wearable,While operators performing tasks with high workload can increase task performance in response to limited increases in cognitive stress, chronic or rapidly accelerating stress can exceed the operator’s ability to compensate, generating acute cognitive strain (ACS). ACS represents a state wherein performance, situation awareness and cooperativity deteriorate markedly, leading to critical errors, mishaps or casualties. Nearly two decades of augmented cognition (AugCog) research has demonstrated the utility of psychophysiologic sensing and analysis for identification and tracking of changes in cognitive state and to modulate human machine interactions for improving system task performance. The proposed approach leveraged prior efforts to modulate cognitive stress using a multiagent approach to acquire and analyze multiple Psychophysiologic sensory channels, including changes in vocalizations, to create a reliable and non-intrusive Detector of Acute Cognitive Strain (DACS). The DACS system provides an integrated wearable multi-modal Research Sensor Suite (RSS) using the open-source Adaptive Multiagent Integration (AMI) architecture, that includes analysis agents for electroencephalograph (EEG), electromyography (EMG), video oculography (VOG), vocalization, and others to identify and correlate physiological signatures with cognitive stress and strain. An online AMI agent-based processing algorithm was developed and applied to audio communications to evaluate for changes in speaker vocalization fundamental frequency (F0) and cadence (utterances per minute). This paper describes initial phase results of aerospace mishap vocalization stress marker detection, a potential element of the proposed DACS system. DACS could use these markers to trigger adaptive automation agents that reduce task load and allow pilots to prevent or recover from ACS episodes. |
2017 |
Mills, Caitlin; Fridman, Igor; Soussou, Walid; Waghray, Disha; Olney, Andrew M; D'Mello, Sidney K Put your thinking cap on: detecting cognitive load using EEG during learning Conference Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 2017. @conference{mills2017put,Current learning technologies have no direct way to assess students' mental effort: are they in deep thought, struggling to overcome an impasse, or are they zoned out? To address this challenge, we propose the use of EEG-based cognitive load detectors during learning. Despite its potential, EEG has not yet been utilized as a way to optimize instructional strategies. We take an initial step towards this goal by assessing how experimentally manipulated (easy and difficult) sections of an intelligent tutoring system (ITS) influenced EEG-based estimates of students' cognitive load. We found a main effect of task difficulty on EEG-based cognitive load estimates, which were also correlated with learning performance. Our results show that EEG can be a viable source of data to model learners' mental states across a 90-minute session. |
2012 |
Soussou, Walid; Rooksby, Michael; Forty, Charles; Weatherhead, James; Marshall, Sandra EEG and eye-tracking based measures for enhanced training Conference 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE 2012, ISSN: 1557-170X. @conference{soussou2012eeg,This paper describes a project whose goal was to establish the feasibility of using unobtrusive cognitive assessment methodologies in order to optimize efficiency and expediency of training. QUASAR, EyeTracking, Inc. (ETI), and Safe Passage International (SPI), teamed to demonstrate correlation between EEG and eye-tracking based cognitive workload, performance assessment and subject expertise on XRay screening tasks. Results indicate significant correlation between cognitive workload metrics based on EEG and eye-tracking measurements recorded during a simulated baggage screening task and subject expertise and error rates in that same task. These results suggest that cognitive monitoring could be useful in improving training efficiency by enabling training paradigms that adapts to increasing expertise. |
2011 |
McDonald, Neil J; Soussou, Walid Quasar's qstates cognitive gauge performance in the cognitive state assessment competition 2011 Conference 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE IEEE, 2011, ISSN: 1557-170X. @conference{mcdonald2011quasar,The Cognitive State Assessment Competition 2011 was organized by the U.S. Air Force Research Laboratory (AFRL) to compare the performance of real-time cognitive state classification software. This paper presents results for QUASAR's data classification module, QStates, which is a software package for real-time (and off-line) analysis of physiologic data collected during cognitive-specific tasks. The classifier's methodology can be generalized to any particular cognitive state; QStates identifies the most salient features extracted from EEG signals recorded during different cognitive states or loads. |
2010 |
Estepp, Justin R; Monnin, Jason W; Christensen, James C; Wilson, Glenn F Evaluation of a Dry Electrode System for Electroencephalography: Applications for Psychophysiological Cognitive Workload Assessment Journal Article In: vol. 54, no. 3, pp. 210–214, 2010. @article{estepp2010evaluation,Advances in state-of-the-art dry electrode technology have led to the development of a novel dry electrode system for electroencephalography (QUASAR, Inc.; San Diego, California, USA). While basic systems-level testing and comparison of this dry electrode system to conventional wet electrode systems has proved to be very favorable, very limited data has been collected that demonstrates the ability of QUASAR's dry electrode system to replicate results produced in more applied, dynamic testing environments that may be used for human factors applications. In this study, QUASAR's dry electrode headset was used in combination with traditional wet electrodes to determine the ability of the dry electrode system to accurately differentiate between varying levels of cognitive workload. Results show that the accuracy in cognitive workload assessment obtained with wet electrodes is comparable to that obtained with the dry electrodes. |
Fielder, James Electroencephalogram (EEG) Study of Learning Effects across Addition Problems Technical Report PEBL Technical Report Series 2010. @techreport{fielder2010electroencephalogram, |
2009 |
Matthews, R; Turner, PJ; McDonald, NJ; Ermolaev, K; Manus, T Mc; Shelby, RA; Steindorf, M Physiological Sensor Suite Using Zero Preparation Hybrid Electrodes for Real Time Workload Classification Journal Article In: The International Test and Evaluation Association, vol. 30, pp. 13–17, 2009. @article{matthews2009physiological,Quantum Applied Science and Research is working closely with the Aberdeen Test Center to develop an integrated system to monitor warfighter physiology. This need has been recognized by two recent major programs: the Defense Advanced Research Projects Agency’s Augmented Cognition program and the U.S. Army’s Warfighter Physiological Status Monitor program. However, these programs were limited by inadequate development of fully deployable noninvasive sensors and in the number of physiological variables they could simultaneously measure. Warfighters need to rapidly perceive, comprehend, and translate combat information into action. To aid them, robust gauges have been developed for classification of cognitive workload, engagement, and fatigue, which simplify complex physiological data into onedimensional parameters that can be used to identify a subject’s cognitive state during the varied tasks carried out in a training environment. This article describes the two main hardware modules that form part of an integrated Physiological Sensor Suite (PSS): a Physiological Status Monitor (PSM) and a module for the measurement of electroencephalograms (EEGs). The PSS is based on revolutionary noninvasive bioelectric sensor technologies. No modification of the skin’s outer layer is required for the operation of this sensor technology, unlike conventional electrode technology that requires the use of conductive pastes or gels, often with abrasive skin preparation of the electrode site. The PSS was designed to be wearable and unobtrusive, with an emphasis on the capability of long-term monitoring of physiological signals. These factors are of considerable importance in operational settings where high end-user compliance is required. The PSM is a simple belt that is worn around the chest. The EEG system has already been incorporated into a soldier’s Kevlar helmet and tested successfully during combat training. Data are acquired using a miniature, ultralowpower, microprocessor-controlled multichannel data acquisition (DAQ) unit that transmits data wirelessly to a base station/data logger worn by the subject. The DAQ unit is worn on the body close to the measurement point, reducing the amount of cable clutter and minimizing the impact on subject mobility without introducing motion artifacts. |
2008 |
Matthews, R; Turner, PJ; McDonald, NJ; Ermolaev, K; Manus, T Mc; Shelby, RA; Steindorf, M Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes Conference 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE 2008. @conference{matthews2008real,This paper describes a compact, lightweight and ultra-low power ambulatory wireless EEG system based upon QUASAR's innovative noninvasive bioelectric sensor technologies. The sensors operate through hair without skin preparation or conductive gels. Mechanical isolation built into the harness permits the recording of high quality EEG data during ambulation. Advanced algorithms developed for this system permit real time classification of workload during subject motion. Measurements made using the EEG system during ambulation are presented, including results for real time classification of subject workload |
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