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• W2945494586 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." @default.
• W2945494586 created "2019-05-29" @default.
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• W2945494586 date "2019-05-17" @default.
• W2945494586 modified "2023-09-30" @default.
• W2945494586 title "Multiple Transferable Recursive Feature Elimination Technique for Emotion Recognition Based on EEG Signals" @default.
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• W2945494586 doi "https://doi.org/10.3390/sym11050683" @default.
• W2945494586 hasPublicationYear "2019" @default.
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