Neuroscience Based Design: Fundamentals and Applications

Neuroscience-based or neuroscience-informed design is a new application area of Brain-Computer Interaction (BCI). It takes its roots in study of human well-being in architecture, human factors study in engineering and manufacturing including neuroergonomics. In traditional human factors studies and/or well-being study, mental workload, stress, and emotion are obtained through questionnaires that are administered upon completion of some task and/or the whole experiment. Recent advances in BCI research allow for using Electroencephalogram (EEG) based brain state recognition algorithms to assess the interaction between brain and human performance. We propose and develop an EEG-based system CogniMeter to monitor and analyze human factors measurements of newly designed software/hardware systems and/or working places. Machine learning techniques are applied to the EEG data to recognize levels of mental workload, stress and emotions during each task. The EEG is used as a tool to monitor and record the brain states of subjects during human factors study experiments. We describe two applications of CogniMeter system: human performance assessment in maritime simulator and EEG-based human factors evaluation in Air Traffic Control (ATC) workplace. By utilizing the proposed EEG-based system, true understanding of subjects working patterns can be obtained. Based on the analyses of the objective real time EEG-based data together with the subjective feedback from the subjects, we are able to reliably evaluate current systems/hardware and/or working place design and refine new concepts and design of future systems.

[1]  B. Cain.  A Review of the Mental Workload Literature , 2007 .

[2]  Olga Sourina,et al.  Real-Time Subject-Dependent EEG-Based Emotion Recognition Algorithm , 2014, Trans. Comput. Sci..

[3]  Olga Sourina,et al.  CogniMeter: EEG-based Emotion, Mental Workload and Stress Visual Monitoring , 2015, 2015 International Conference on Cyberworlds (CW).

[4]  Charles Spence,et al.  Drive safely with neuroergonomics , 2012 .

[5]  Glenn F. Wilson,et al.  Topographical changes in the ongoing EEG related to the difficulty of mental tasks , 2005, Brain Topography.

[6]  Samara L. Firebaugh,et al.  Cognitive stress recognition , 2013, 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC).

[7]  Olga Sourina,et al.  EEG Based Stress Monitoring , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[8]  Olga Sourina,et al.  Real-time EEG-based emotion monitoring using stable features , 2015, The Visual Computer.

[9]  Scott Makeig,et al.  Emotion Recognition from EEG during Self-Paced Emotional Imagery , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[10]  N. Bevan,et al.  Quality in use: incorporating human factors into the software engineering lifecycle , 1997, Proceedings of IEEE International Symposium on Software Engineering Standards.

[11]  Earl S. Stein,et al.  The Use of Predictive Displays for Aiding Controller Situation Awareness , 1999 .

[12]  Martin Buss,et al.  Feature Extraction and Selection for Emotion Recognition from EEG , 2014, IEEE Transactions on Affective Computing.

[13]  Raja Parasuraman,et al.  Neuroergonomics: The Brain at Work , 2006 .

[14]  G G Denton.  The Influence of Visual Pattern on Perceived Speed , 1980, Perception.

[15]  Olga Sourina,et al.  Human Factor Study for Maritime Simulator-Based Assessment of Cadets , 2016 .

[16]  Tomas Ros,et al.  Optimizing microsurgical skills with EEG neurofeedback , 2009, BMC Neuroscience.

[17]  Hyo Jong Lee,et al.  EEG based Emotion Recognition from Human Brain using Hjorth Parameters and SVM , 2015, BSBT 2015.

[18]  S. A. Hosseini,et al.  Emotional Stress Recognition System Using EEG and Psychophysiological Signals: Using New Labelling Process of EEG Signals in Emotional Stress State , 2010, 2010 International Conference on Biomedical Engineering and Computer Science.

[19]  Hankins Tc,et al.  A comparison of heart rate, eye activity, EEG and subjective measures of pilot mental workload during flight. , 1998, Aviation, space, and environmental medicine.

[20]  Olga Sourina,et al.  EEG-based mental workload recognition related to multitasking , 2015, 2015 10th International Conference on Information, Communications and Signal Processing (ICICS).

[21]  Grigore C. Burdea,et al.  Virtual reality training simulation for palpation of subsurface tumors , 1997, Proceedings of IEEE 1997 Annual International Symposium on Virtual Reality.

[22]  Dinesh Kant Kumar,et al.  Evaluation of feature extraction techniques in emotional state recognition , 2012, 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI).

[23]  Wei Tech Ang,et al.  EEG-Based Human Factors Evaluation of Conflict Resolution Aid and Tactile User Interface in Future Air Traffic Control Systems , 2017 .

[24]  Teodiano Freire Bastos Filho,et al.  Methodology for analysis of stress level based on asymmetry patterns of alpha rhythms in EEG signals , 2014, 5th ISSNIP-IEEE Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC).

[25]  Alan Gevins,et al.  Electroencephalography (EEG) in Neuroergonomics , 2006, Neuroergonomics.

[26]  J. Reason,et al.  Human factors and cardiac surgery: a multicenter study. , 2000, The Journal of thoracic and cardiovascular surgery.

[27]  M. Just,et al.  A decrease in brain activation associated with driving when listening to someone speak , 2008, Brain Research.

[28]  Olga Sourina,et al.  Individual Theta/Beta Based Algorithm for Neurofeedback Games to Improve Cognitive Abilities , 2015, Trans. Comput. Sci..

[29]  Linda Ng Boyle,et al.  Variability of Driving Performance During Microsleeps , 2017 .

[30]  Raja Parasuraman,et al.  Neuroergonomics: Research and practice , 2003 .

[31]  Han-Shue Tan,et al.  Design and field testing of a Cooperative Adaptive Cruise Control system , 2010, Proceedings of the 2010 American Control Conference.

[32]  Olga Sourina,et al.  Driver Workload Detection in On-Road Driving Environment Using Machine Learning , 2015 .

[33]  Michael E. Smith,et al.  Monitoring Working Memory Load during Computer-Based Tasks with EEG Pattern Recognition Methods , 1998, Hum. Factors.

[34]  Neville A. Stanton,et al.  Human Factors In Consumer Products , 1997 .