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Coupled Projection Transfer Metric Learning for Cross-Session Emotion Recognition from EEG.
- Source :
- Systems; Apr2022, Vol. 10 Issue 2, pN.PAG-N.PAG, 19p
- Publication Year :
- 2022
-
Abstract
- Distribution discrepancies between different sessions greatly degenerate the performance of video-evoked electroencephalogram (EEG) emotion recognition. There are discrepancies since the EEG signal is weak and non-stationary and these discrepancies are manifested in different trails in each session and even in some trails which belong to the same emotion. To this end, we propose a Coupled Projection Transfer Metric Learning (CPTML) model to jointly complete domain alignment and graph-based metric learning, which is a unified framework to simultaneously minimize cross-session and cross-trial divergences. By experimenting on the SEED_IV emotional dataset, we show that (1) CPTML exhibits a significantly better performance than several other approaches; (2) the cross-session distribution discrepancies are minimized and emotion metric graph across different trials are optimized in the CPTML-induced subspace, indicating the effectiveness of data alignment and metric exploration; and (3) critical EEG frequency bands and channels for emotion recognition are automatically identified from the learned projection matrices, providing more insights into the occurrence of the effect. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20798954
- Volume :
- 10
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- Systems
- Publication Type :
- Academic Journal
- Accession number :
- 156599289
- Full Text :
- https://doi.org/10.3390/systems10020047