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- Title
Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals.
- Authors
Cai, Jiahui; Chen, Wei; Yin, Zhong
- 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.
- Subjects
EMOTION recognition; BRAIN-computer interfaces; ELECTROENCEPHALOGRAPHY; BIOMEDICAL signal processing; SUPPORT vector machines; FEATURE selection; LEAST squares
- Publication
Symmetry (20738994), 2019, Vol 11, Issue 5, p683
- ISSN
2073-8994
- Publication type
Article
- DOI
10.3390/sym11050683