Hag, A.; Handayani, D.; Altalhi, M.; Pillai, T.; Mantoro, T.; Kit, M.H.; Al-Shargie, F. Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm. Sensors2021, 21, 8370.
Hag, A.; Handayani, D.; Altalhi, M.; Pillai, T.; Mantoro, T.; Kit, M.H.; Al-Shargie, F. Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm. Sensors 2021, 21, 8370.
Hag, A.; Handayani, D.; Altalhi, M.; Pillai, T.; Mantoro, T.; Kit, M.H.; Al-Shargie, F. Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm. Sensors2021, 21, 8370.
Hag, A.; Handayani, D.; Altalhi, M.; Pillai, T.; Mantoro, T.; Kit, M.H.; Al-Shargie, F. Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm. Sensors 2021, 21, 8370.
Abstract
Mental stress state recognition using electroencephalogram (EEG) signals for real-life applications needs a conventional wearable device. This requires an efficient number of EEG channels and an optimal feature set. The main objective of the study is to identify an optimal feature subset that can best discriminate mental stress states while enhancing the overall performance. Thus, multi-domain feature extraction methods were employed, namely, time domain, frequency domain, time-frequency domain, and network connectivity features, to form a large feature vector space. To avoid the computational complexity of high dimensional space, a hybrid feature selection (FS) method of minimum Redundancy Maximum Relevance with Particle Swarm Optimization and Support Vector Machine (mRMR-PSO-SVM) is proposed to remove noise, redundant, and irrelevant features and keep the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art heuristic methods. The proposed model has significantly reduced the features vector space by an average of 70% in comparison to the state-of-the-art methods while significantly increasing overall detection performance.
Medicine and Pharmacology, Psychiatry and Mental Health
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
