Application of EEG Metrics in the Decision-Making Process

The decision-making process is a complex task uses the multi-criteria methods in the formalized decision support. Decisions are direct reflection of decision maker preferences. Multi-criteria methods use different methodological approaches (algorithms) to determine the final assessment of decision variants (e.g., ranking). Decision maker must do many actions (partial evaluations) in some of these methods. Issues of the decision maker’s engagement in the assessment process arise which can be identified using measurements by EEG. It is possible to identify various internal processes occurring with the decision maker during individual stages of the calculation procedure. Various types of EEG metrics are used for this, such as the index of frontal asymmetry, engagement, distraction, etc.

[1]  John J. B. Allen,et al.  Assessing and conceptualizing frontal EEG asymmetry: An updated primer on recording, processing, analyzing, and interpreting frontal alpha asymmetry. , 2017, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[2]  Michael E. Smith,et al.  Neurophysiologic monitoring of mental workload and fatigue during operation of a flight simulator , 2005, SPIE Defense + Commercial Sensing.

[3]  Stelios H. Zanakis,et al.  Multi-attribute decision making: A simulation comparison of select methods , 1998, Eur. J. Oper. Res..

[4]  Rudolf Grünig,et al.  Successful Decision-Making: A Systematic Approach to Complex Problems , 2005 .

[5]  M. Paradiso,et al.  Neuroscience: Exploring the Brain , 1996 .

[6]  M. Piwowarski,et al.  Application of the Vector Measure Construction Method and Technique for Order Preference by Similarity Ideal Solution for the Analysis of the Dynamics of Changes in the Poverty Levels in the European Union Countries , 2018, Sustainability.

[7]  Rui Neves-Silva,et al.  First Look at MCDM: Choosing a Decision Method , 2012 .

[8]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[9]  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.

[10]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[11]  Zhi Pei,et al.  Rational decision making models with incomplete weight information for production line assessment , 2013, Inf. Sci..

[12]  N. Kamel,et al.  Deep in thought while driving: An EEG study on drivers’ cognitive distraction , 2014 .

[13]  P. Pintrich,et al.  Motivational and self-regulated learning components of classroom academic performance. , 1990 .

[14]  Chin-Teng Lin,et al.  EEG-based brain dynamics of driving distraction , 2011, The 2011 International Joint Conference on Neural Networks.

[15]  Igor Linkov,et al.  Multi-criteria decision analysis in environmental sciences: ten years of applications and trends. , 2011, The Science of the total environment.

[16]  David N. Towers,et al.  Resting frontal EEG asymmetry as an endophenotype for depression risk: sex-specific patterns of frontal brain asymmetry. , 2010, Journal of abnormal psychology.

[17]  Yingxu Wang,et al.  The Theoretical Framework of Cognitive Informatics , 2007, Int. J. Cogn. Informatics Nat. Intell..

[18]  Maarten A. S. Boksem,et al.  Absorbed in the task: Personality measures predict engagement during task performance as tracked by error negativity and asymmetrical frontal activity , 2010, Cognitive, affective & behavioral neuroscience.

[19]  S. Uijtdehaage,et al.  Assessing the accuracy of topographic EEG mapping for determining local brain function. , 1998, Electroencephalography and clinical neurophysiology.

[20]  Jarosław Wątróbski,et al.  Research on the Properties of the AHP in the Environment of Inaccurate Expert Evaluations , 2016 .

[21]  Robin Nusslock,et al.  Cognitive vulnerability and frontal brain asymmetry: common predictors of first prospective depressive episode. , 2011, Journal of abnormal psychology.

[22]  Daphne N. Yu,et al.  High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. , 1997, Cerebral cortex.

[23]  Mariusz Borawski Use of Computer Game as an Element of Social Campaign Focusing Attention on Reliability of Information in the Internet , 2017 .

[24]  Viktor Müller,et al.  Cortical EEG correlates of successful memory encoding: Implications for lifespan comparisons , 2006, Neuroscience & Biobehavioral Reviews.

[25]  T. Saaty Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process , 2000 .

[26]  Christopher Summerfield,et al.  Coherent theta-band EEG activity predicts item-context binding during encoding , 2005, NeuroImage.

[27]  Thomas D. Parsons,et al.  Evaluating Electroencephalography Engagement Indices During Video Game Play , 2015, FDG.

[28]  E. Toms,et al.  What is user engagement? A conceptual framework for defining user engagement with technology , 2008, J. Assoc. Inf. Sci. Technol..

[29]  S French,et al.  Multicriteria Methodology for Decision Aiding , 1996 .

[30]  A. Pope,et al.  Biocybernetic system evaluates indices of operator engagement in automated task , 1995, Biological Psychology.

[31]  Thomas E. Joiner,et al.  Affective responses to EEG preparation and their link to resting anterior EEG asymmetry , 2002 .

[32]  R. Davidson What does the prefrontal cortex “do” in affect: perspectives on frontal EEG asymmetry research , 2004, Biological Psychology.

[33]  W. Edwards,et al.  Decision Analysis and Behavioral Research , 1986 .

[34]  Jarosław Wątróbski,et al.  Multi-criteria decision support for planning and evaluation of performance of viral marketing campaigns in social networks , 2018, PloS one.

[35]  P. Nunez,et al.  Electric fields of the brain , 1981 .

[36]  Philip A. Gable,et al.  The role of asymmetric frontal cortical activity in emotion-related phenomena: A review and update , 2010, Biological Psychology.

[37]  F. Freeman,et al.  Evaluation of an adaptive automation system using three EEG indices with a visual tracking task , 1999, Biological Psychology.

[38]  Jeffrey B. Henriques,et al.  Asymmetrical brain electrical activity discriminates between psychometrically-matched verbal and spatial cognitive tasks. , 1990, Psychophysiology.

[39]  M. Csíkszentmihályi Flow. The Psychology of Optimal Experience. New York (HarperPerennial) 1990. , 1990 .

[40]  Garima Bajwa,et al.  Detecting driver distraction using stimuli-response EEG analysis , 2019, ArXiv.

[41]  Thomas L. Saaty,et al.  Decision making with dependence and feedback : the analytic network process : the organization and prioritization of complexity , 1996 .

[42]  Robert Plonsey,et al.  Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields , 1995 .

[43]  David N. Towers,et al.  A better estimate of the internal consistency reliability of frontal EEG asymmetry scores. , 2009, Psychophysiology.

[44]  Michael D. Robinson,et al.  Measures of emotion: A review , 2009, Cognition & emotion.

[45]  G. Schwartz,et al.  Differential lateralization for positive and negative emotion in the human brain: EEG spectral analysis , 1985, Neuropsychologia.

[46]  Jacek Cypryjański,et al.  Expressing Our Preferences with the Use of AHP: The Game Is not Worth the Candle? , 2017 .

[47]  John J. B. Allen,et al.  Frontal EEG asymmetry as a moderator and mediator of emotion , 2004, Biological Psychology.

[48]  F. Yamada Frontal midline theta rhythm and eyeblinking activity during a VDT task and a video game: useful tools for psychophysiology in ergonomics. , 1998, Ergonomics.

[49]  Artur Karczmarczyk,et al.  Generalised framework for multi-criteria method selection , 2018, Omega.

[50]  F. Cincotti,et al.  Neural Basis for Brain Responses to TV Commercials: A High-Resolution EEG Study , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.