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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Khosrowabadi, R. Chai Quek Kai Keng Ang Sau Wai Tung Heijnen, M. |
| Copyright Year | 2011 |
| Description | Author affiliation: Institute for Infocomm Research, A*STAR, 1 Fusionopolis Way, #21-01 Connexis, Singapore 138632 (Kai Keng Ang) || School of Medical Technology, Zuyd University, Nieuw Eyckholt 300, 6419 DJ, Heerlen, The Netherlands (Heijnen, M.) || Center for Computational Intelligence, Division of Computer Science, School of Computer Engineering, Nanyang Technological University, Singapore 639798 (Khosrowabadi, R.; Chai Quek; Sau Wai Tung) |
| 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 are 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. |
| Starting Page | 757 |
| Ending Page | 762 |
| File Size | 472399 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424496358 |
| ISSN | 21614407 |
| e-ISBN | 9781424496372 |
| DOI | 10.1109/IJCNN.2011.6033297 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-07-31 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Electroencephalography Stress Feature extraction Accuracy Sensitivity Support vector machines Educational institutions |
| Content Type | Text |
| Resource Type | Article |
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