Selection of Neural Oscillatory Features for Human Stress Classification with Single Channel EEG Headset

A study on classification of psychological stress in humans using electroencephalography (EEG) is presented. The stress is classified using a correlation-based feature subset selection method that efficiently reduces the feature vector length. In this study, twenty-eight participants are involved by filling in the perceived stress scale-10 (PSS-10) questionnaire and their EEG is also recorded in closed eye condition to measure the baseline stress. The recorded data is labelled on the basis of the stress level that is indicated by the participant's PSS score. The feature selection method has shown that, among the EEG oscillations, low beta, high beta, and low gamma are the most significant neural oscillations for classifying human stress. The proposed method not only reduces the time to build a classification model but also improves the classification accuracy up to 78.57% using a single channel wearable EEG device.

[1]  Mohd Nasir Taib,et al.  Brainwaves stress pattern based on perceived stress scale test , 2015, 2015 IEEE 6th Control and System Graduate Research Colloquium (ICSGRC).

[2]  T. Kamarck,et al.  A global measure of perceived stress. , 1983, Journal of health and social behavior.

[3]  F. Seguin,et al.  Effects of stress throughout the lifespan on the brain, behaviour and cognition , 2009 .

[4]  Aljo Mujcic,et al.  Mental workload vs. stress differentiation using single-channel EEG , 2017 .

[5]  Jens C. Pruessner,et al.  City living and urban upbringing affect neural social stress processing in humans , 2011, Nature.

[6]  Syed Muhammad Anwar,et al.  Quantification of Human Stress Using Commercially Available Single Channel EEG Headset , 2017, IEICE Trans. Inf. Syst..

[7]  J. Morrison,et al.  The Brain on Stress: Vulnerability and Plasticity of the Prefrontal Cortex over the Life Course , 2013, Neuron.

[8]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[9]  Nicole Y. Weekes,et al.  The effect of a naturalistic stressor on frontal EEG asymmetry, stress, and health , 2007, Biological Psychology.

[10]  Syed Muhammad Anwar,et al.  Human emotion recognition and analysis in response to audio music using brain signals , 2016, Comput. Hum. Behav..

[11]  R. E. Wheeler,et al.  Frontal brain asymmetry and emotional reactivity: a biological substrate of affective style. , 2007, Psychophysiology.

[12]  Hiie Hinrikus,et al.  Electroencephalographic spectral asymmetry index for detection of depression , 2009, Medical & Biological Engineering & Computing.

[13]  H.T. Nguyen,et al.  Detecting neural changes during stress and fatigue effectively: a comparison of spectral analysis and sample entropy , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[14]  Wai Chong Chia,et al.  Analysis of Single-Electrode EEG Rhythms Using MATLAB to Elicit Correlation with Cognitive Stress , 2015 .

[15]  Syed Muhammad Anwar,et al.  Psychological stress measurement using low cost single channel EEG headset , 2015, 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[16]  Francisco J. Pelayo,et al.  Stress Assessment by Prefrontal Relative Gamma , 2016, Front. Comput. Neurosci..

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

[18]  Zara Mansoor,et al.  Pain Prediction in Humans using Human Brain Activity Data , 2018, WWW.

[19]  Sherif N. Abbas,et al.  A new multi-level approach to EEG based human authentication using eye blinking , 2016, Pattern Recognit. Lett..

[20]  Bin Hu,et al.  Mild Depression Detection of College Students: an EEG-Based Solution with Free Viewing Tasks , 2015, Journal of Medical Systems.

[21]  Tamás D. Gedeon,et al.  Objective measures, sensors and computational techniques for stress recognition and classification: A survey , 2012, Comput. Methods Programs Biomed..

[22]  Jong-Min Song,et al.  Outcomes of patients with stress-induced cardiomyopathy diagnosed by echocardiography in a tertiary referral hospital. , 2010, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[23]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[24]  Syed Muhammad Anwar,et al.  Emotion recognition in response to traditional and tactile enhanced multimedia using electroencephalography , 2018, Multimedia Tools and Applications.

[25]  S. A. Hosseini,et al.  Higher Order Spectra Analysis of EEG Signals in Emotional Stress States , 2010, 2010 Second International Conference on Information Technology and Computer Science.