STEW: Simultaneous Task EEG Workload Data Set

This paper describes an open access electroencephalography (EEG) data set for multitasking mental workload activity induced by a single-session simultaneous capacity (SIMKAP) experiment with 48 subjects. To validate the database, EEG spectral activity was evaluated with EEGLAB and the significant channels and activities for the experiment are highlighted. Classification performance was evaluated by training a support vector regression model on selected features from neighborhood component analysis based on a nine-point workload rating scale. With a reduced feature dimension, 69% classification accuracy was obtained for 3 identified workload levels from the rating scale with Cohen’s kappa of 0.46. Accurate discrimination of mental workload is a desirable outcome in the field of operator performance analysis and BCI development; thus, we hope that our provided database and analyses can contribute to future investigations in this research field.

[1]  Abdulmajeed Dabwan,et al.  Human Error in Multitasking Environments , 2015 .

[2]  Michelle N. Lumicao,et al.  EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. , 2007, Aviation, space, and environmental medicine.

[3]  Jasmin Kevric,et al.  Biomedical Signal Processing and Control , 2016 .

[4]  Glenn F. Wilson,et al.  Real-Time Assessment of Mental Workload Using Psychophysiological Measures and Artificial Neural Networks , 2003, Hum. Factors.

[5]  M. J. E. Salami,et al.  EEG signal classification for real-time brain-computer interface applications: A review , 2011, 2011 4th International Conference on Mechatronics (ICOM).

[6]  Fabio Babiloni,et al.  Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment , 2016, Front. Hum. Neurosci..

[7]  R. Homan,et al.  Cerebral location of international 10-20 system electrode placement. , 1987, Electroencephalography and clinical neurophysiology.

[8]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[9]  Peter A Hancock,et al.  State of science: mental workload in ergonomics , 2015, Ergonomics.

[10]  Klaus-Robert Müller,et al.  Open Access Dataset for EEG+NIRS Single-Trial Classification , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[11]  Cornelius J. König,et al.  Working Memory Dimensions as Differential Predictors of the Speed and Error Aspect of Multitasking Performance , 2006 .

[12]  Kenneth Kwok,et al.  Evaluation Of Cognitive Workload From EEG During A Mental Arithmetic Task , 2011 .

[13]  Scott Makeig,et al.  Estimation of task workload from EEG data: New and current tools and perspectives , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[15]  Christian Mühl,et al.  EEG-based workload estimation across affective contexts , 2014, Front. Neurosci..

[16]  Yamira Santiago-Espada,et al.  The Multi-Attribute Task Battery II (MATB-II) Software for Human Performance and Workload Research: A User's Guide , 2011 .

[17]  Qiang Ji,et al.  Cross-subject workload classification with a hierarchical Bayes model , 2012, NeuroImage.

[18]  T. Jong.  Cognitive load theory, educational research, and instructional design: some food for thought , 2010 .

[19]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[20]  J. Reason.  Human error: models and management , 2000, BMJ : British Medical Journal.

[21]  Kilseop Ryu,et al.  Evaluation of mental workload with a combined measure based on physiological indices during a dual task of tracking and mental arithmetic , 2005 .

[22]  Shiliang Sun,et al.  A review of adaptive feature extraction and classification methods for EEG-based brain-computer interfaces , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[23]  Kristian Lukander,et al.  Estimating Brain Load from the EEG , 2009, TheScientificWorldJournal.

[24]  E. Sellers,et al.  How many people are able to control a P300-based brain–computer interface (BCI)? , 2009, Neuroscience Letters.

[25]  F. Paas.  Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. , 1992 .

[26]  Thomas E. Nygren,et al.  The Subjective Workload Assessment Technique: A Scaling Procedure for Measuring Mental Workload , 1988 .

[27]  P. Sajda,et al.  A data analysis competition to evaluate machine learning algorithms for use in brain-computer interfaces , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  Tzyy-Ping Jung,et al.  Real-time neuroimaging and cognitive monitoring using wearable dry EEG , 2015, IEEE Transactions on Biomedical Engineering.

[29]  Jon Touryan,et al.  A Comparison of Electroencephalography Signals Acquired from Conventional and Mobile Systems , 2014 .

[30]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[31]  Rosa H. M. Chan,et al.  An evaluation of mental workload with frontal EEG , 2017, PloS one.

[32]  Chris Berka,et al.  Evaluation of an EEG workload model in an Aegis simulation environment , 2005, SPIE Defense + Commercial Sensing.

[33]  Wei Yang,et al.  Neighborhood Component Feature Selection for High-Dimensional Data , 2012, J. Comput..

[34]  Kathrin C. J. Eschmann,et al.  Topographical differences of frontal-midline theta activity reflect functional differences in cognitive control abilities , 2018, Brain and Cognition.

[35]  Carolyne R Swain,et al.  EEG power changes during a multiple level memory retention task. , 1999, International Journal of Psychophysiology.

[36]  J. R. Comstock.  MAT - MULTI-ATTRIBUTE TASK BATTERY FOR HUMAN OPERATOR WORKLOAD AND STRATEGIC BEHAVIOR RESEARCH , 1994 .

[37]  Nigel H. Lovell,et al.  Beyond Subjective Self-Rating: EEG Signal Classification of Cognitive Workload , 2015, IEEE Transactions on Autonomous Mental Development.

[38]  Cosima Prahm,et al.  Evaluation of consumer EEG device Emotiv EPOC , 2011 .

[39]  L. Trejo,et al.  EEG-Based Estimation and Classification of Mental Fatigue , 2015 .

[40]  Michael E. Smith,et al.  Neurophysiological measures of cognitive workload during human-computer interaction , 2003 .

[41]  Elham Parvinnia,et al.  Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm , 2014, J. King Saud Univ. Comput. Inf. Sci..

[42]  Xingyu Wang,et al.  Sparse Bayesian Classification of EEG for Brain–Computer Interface , 2016, IEEE Transactions on Neural Networks and Learning Systems.