Classification of Perceived Mental Stress Using A Commercially Available EEG Headband
| Autor: | Muhammad Majid, Aamir Arsalan, Amna Rauf Butt, Syed Muhammad Anwar |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Adult
Male Support Vector Machine Adolescent Computer science Feature extraction Perceived Stress Scale Health Informatics Feature selection Electroencephalography 01 natural sciences Young Adult 03 medical and health sciences Naive Bayes classifier 0302 clinical medicine Health Information Management medicine Humans Electrical and Electronic Engineering medicine.diagnostic_test business.industry 010401 analytical chemistry Bayes Theorem Signal Processing Computer-Assisted Pattern recognition Perceptron 0104 chemical sciences Computer Science Applications Support vector machine Feature (computer vision) Female Artificial intelligence business Stress Psychological 030217 neurology & neurosurgery |
| Zdroj: | IEEE Journal of Biomedical and Health Informatics. 23:2257-2264 |
| ISSN: | 2168-2208 2168-2194 |
| Popis: | Human stress is a serious health concern, which must be addressed with appropriate actions for a healthy society. This paper presents an experimental study to ascertain the appropriate phase, when electroencephalography (EEG) based data should be recorded for classification of perceived mental stress. The process involves data acquisition, pre-processing, feature extraction and selection, and classification. The stress level of each subject is recorded by using a standard perceived stress scale questionnaire, which is then used to label the EEG data. The data are divided into two (stressed and non-stressed) and three (non-stressed, mildly stressed, and stressed) classes. The EEG data of 28 participants are recorded using a commercially available four channel Muse EEG headband in two phases i.e., pre-activity and post-activity. Five feature groups, which include power spectral density, correlation, differential asymmetry, rational asymmetry, and power spectrum are extracted from five bands of each EEG channel. We propose a new feature selection algorithm, which selects features from appropriate EEG frequency band based on classification accuracy. Three classifiers i.e., support vector machine, the Naive Bayes, and multi-layer perceptron are used to classify stress level of the participants. It is evident from our results that EEG recording during the pre-activity phase is better for classifying the perceived stress. An accuracy of $\text{92.85}\%$ and $\text{64.28}\%$ is achieved for two- and three-class stress classification, respectively, while utilizing five groups of features from theta band. Our proposed feature selection algorithm is compared with existing algorithms and gives better classification results. |
| Databáze: | OpenAIRE |
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