A Brain-Computer Interface for classifying EEG correlates of chronic mental stress Reza Khosrowabadi, Chai Quek, Kai Keng Ang, Sau Wai Tung, and Michel Heijnen
Abstract— In this paper, a Brain-Computer Interface (BCI) for classifying EEG correlates of chronic mental stress is proposed. Data from 8 EEG channels are collected from 26 healthy right-handed students during university examination period and after the examination whereby the former is considered to be relatively more stressful to students than the latter. The mental stress level are measured using the Perceived Stress Scale 14 (PSS-14) and categorized into stressed and stress-free groups. The proposed BCI is then used to classify the subjects' mental stress level on EEG features extracted using the Higuchi’s fractal dimension of EEG, Gaussian mixtures of EEG spectrogram, and Magnitude Square Coherence Estimation (MSCE) between the EEG channels. Classification on the EEG features is then performed using the K-Nearest Neighbor (K-NN) and Support Vector Machine (SVM). The performance of the proposed BCI is then evaluated from the inter-subject classification accuracy using leave-one-out validation. The results showed that the proposed BCI using features extracted by MSCE yielded a promising inter-subject validation accuracy of over 90% in classifying the EEG correlates of chronic mental stress. I. INTRODUCTION stress is a major health problem in most Mental industrialized country in the present day. Although there
is no precise definition for stress, the concept of stress is frequently used in daily life. Stress originates from different sources in response to psychosocial factors, such as time pressure, responsibility; economic problems or physical factors, such as noise and heat; or biological conditions and psychological factors. Stress influences various bodily functions and can be acute (short-term) or chronic (longterm). Acute stress is usually not a health risk but chronic stress is an important issue in today’s society because it causes a wide variety of health problems [1]. This paper proposes a non-invasive Brain-Computer Interface to classify the EEG correlates of chronic mental stress. In this study, data from 8 EEG channels are collected from 26 healthy right-handed students in eyes-close and eyes-open condition during university examination period and after the examination period. The eyes-close condition R. Khosrowabadi, C. Quek and S. W. Tung are with the Center for Computational Intelligence, Division of Computer Science, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798 (e-mail: {reza0004, ashcquek, tung0007}@ntu.edu.sg). K. K. Ang is with the Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis, Singapore 138632 (email:
[email protected]). M. Heijnen is with the School of Medical Technology, Zuyd University, Nieuw Eyckholt 300, 6419 DJ, Heerlen, The Netherlands (e-mail:
[email protected]).
investigates the EEG correlates of mental stress in the resting state, while the eyes-open condition investigates the EEG correlates of mental stress in a similar mental state whereby the subjects were asked to look at a brainmarker logo at the center of a blue screen. This is to investigate whether the resting state or a similar resting state is more suitable for detecting the level of mental stress of the subjects. In this study, the chronic mental stress level is measured by the Perceived Stress Scale 14 (PSS-14) [2] that comprises 14 items of questionnaire that ask the subject how often certain experiences of stress occurred in the last month. The response for each item ranges from 0 to 4, thus the range of the total response is from 0 to 56 with a higher score correlating with a higher mental stress level. The PSS is designed for use by subjects with at least a junior high school education whereby the items in the questionnaire are easy to understand and the responses are simple. The overall mean and standard deviation of the PSS-14 total response from the subjects collected was 24.5, 6.7 . A total score lower than -/2=21 is considered to be relatively stress-free whereas a total response score higher than +/2=28 is considered to be relatively stressed. The features from the EEG correlates of mental stress are then extracted using Higuchi's fractal dimension of EEG, Gaussian mixtures of EEG spectrogram, and Magnitude Squared Coherence Estimation (MSCE) between the EEG channels on the subjects categorized into the stress-free and stressed groups in the eyes-close and eyes-open conditions. The features are then classified the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) and the performance of the proposed BCI is then evaluated from the inter-subject classification accuracy using leave-one out validation. The remainder of this paper is as follows. Section II describes experimental protocol in more detail. Section III describes the signal processing, feature extraction and classification of the proposed BCI system for classifying the EEG correlates of chronic mental stress. Section IV presents the experimental results and finally section V concludes the paper. II. EXPERIMENTAL DESIGN This section describes the experimental design to collect the EEG data in this study. The protocol of the experiment is shown in Fig 1.
International 10-20 System EEG electrode placement Fig 1. Protocol for collecting EEG data
The EEG data was collected with the subject seated in a comfortable chair in a registration room whereby the experimental procedure was explained to the subject and informed consent was sought. Eight Ag/AgCl electrodes were then attached bilaterally on the subject's scalp using the 10/20 system of electrode placement. The EEG was recorded using the BIMEC device (Brainmarker BV, The Netherlands) with a sampling rate of 250 Hz. The impedance of recording electrodes was monitored for each subject prior to data collection and it was kept below 10 k. The subject was asked to close his or her eyes for 5 minutes, followed by a break of 30 seconds. The subject was then asked to look at a brainmarker logo at the centre of a blue screen in the eyesopen condition for 1 minute. The subject was then asked to fill in a PSS-14 and handedness questionnaires [3]. The EEG data were collected from 26 healthy right-handed subjects recruited from the university (age ranged: 18-30, 6 females and 20 males). The examination period is considered to be relatively more stressful to university students than the period after examination. Hence EEG data were collected during the examination period and two weeks after the last examination. In this study, a total of 15 students were recruited during the university examination period and 11 students after the university examination period. Fig 2 shows histogram distribution of the subjects’ PSS-14 responses. Based on their PSS-14 responses, 8 stressed (PSS-1428) and 10 nonstressed (PSS-1421) students are then included to investigate the performance of the proposed BCI for classifying the EEG correlates of chronic mental stress. The data from the remaining subjects (21
