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EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model
Open AccessArticle

A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals

Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan 44610, Korea
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Brain Sci. 2019, 9(12), 376; https://doi.org/10.3390/brainsci9120376
Received: 10 October 2019 / Revised: 11 December 2019 / Accepted: 12 December 2019 / Published: 13 December 2019
(This article belongs to the Special Issue Brain Plasticity, Cognitive Training and Mental States Assessment)
Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain-specific information pool to develop an effective machine learning model. In this study, a multi-domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid feature pool contains features from two types of analysis: (a) statistical parametric analysis from the time domain, and (b) wavelet-based bandwidth specific feature analysis from the time-frequency domain. Then, a wrapper-based feature selector, Boruta, is applied for ranking all the relevant features from that feature pool instead of considering only the non-redundant features. Finally, the k-nearest neighbor (k-NN) algorithm is used for final classification. The proposed model yields an overall accuracy of 73.38% for the total considered dataset. To validate the performance of the proposed model and highlight the necessity of designing a hybrid feature pool, the model was compared to non-linear dimensionality reduction techniques, as well as those without feature ranking. View Full-Text
Keywords: EEG signals; stress analysis; feature selector; k-NN EEG signals; stress analysis; feature selector; k-NN
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Hasan, M.J.; Kim, J.-M. A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals. Brain Sci. 2019, 9, 376.

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