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A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals.
- Source :
-
Brain Sciences (2076-3425) . Dec2019, Vol. 9 Issue 12, p376-376. 1p. - Publication Year :
- 2019
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20763425
- Volume :
- 9
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Brain Sciences (2076-3425)
- Publication Type :
- Academic Journal
- Accession number :
- 140944052
- Full Text :
- https://doi.org/10.3390/brainsci9120376