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Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals

1,2, 1,2,* and 1,2
1
School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2
Engineering Research Center of Optical Instrument and System, Ministry of Education, Shanghai Key Lab of Modern Optical System, University of Shanghai for Science and Technology, Shanghai 200093, China
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(5), 683; https://doi.org/10.3390/sym11050683
Received: 18 April 2019 / Revised: 7 May 2019 / Accepted: 15 May 2019 / Published: 17 May 2019
PDF [657 KB, uploaded 17 May 2019]

Abstract

Feature selection plays a crucial role in analyzing huge-volume, high-dimensional EEG signals in human-centered automation systems. However, classical feature selection methods pay little attention to transferring cross-subject information for emotions. To perform cross-subject emotion recognition, a classifier able to utilize EEG data to train a general model suitable for different subjects is needed. However, existing methods are imprecise due to the fact that the effective feelings of individuals are personalized. In this work, the cross-subject emotion recognition model on both binary and multi affective states are developed based on the newly designed multiple transferable recursive feature elimination (M-TRFE). M-TRFE manages not only a stricter feature selection of all subjects to discover the most robust features but also a unique subject selection to decide the most trusted subjects for certain emotions. Via a least square support vector machine (LSSVM), the overall multi (joy, peace, anger and depression) classification accuracy of the proposed M-TRFE reaches 0.6513, outperforming all other methods used or referenced in this paper.
Keywords: emotion recognition; effective computing; physiological signals; recursive feature elimination; EEG emotion recognition; effective computing; physiological signals; recursive feature elimination; EEG
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 (CC BY 4.0).
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Cai, J.; Chen, W.; Yin, Z. Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals. Symmetry 2019, 11, 683.

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