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

Jiahui Cai,Zhong Yin,Wei Chen

doi:10.3390/sym11050683

Jiahui Cai, Zhong Yin + Show 1 more

Open Access

https://doi.org/10.3390/sym11050683

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Journal: Symmetry	Publication Date: May 17, 2019
Citations: 18	License type: CC BY 4.0

Affiliation: University of Shanghai for Science and Technology

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.

Highlights

Emotions are known as a group of intrinsic cognitive states of the human mind
In a series of previous works, we investigated OFS classification using least square support vector machine (LSSVM) based on recursive feature selection (RFE) [34]
As the training/test set was completely different between each repetition, the subject-specific emotion recognition was run for five times to test the stability of the random use of the working segment

Summary

Introduction

Emotions are known as a group of intrinsic cognitive states of the human mind. It adds meanings to human activities and plays a vital role in human communication, intelligence, and perception [1].An emotion can be triggered by a specific feeling and will eventually lead to a change in behavior [2].Since emotions are closely associated with human activities and psychophysiological states, establishing intelligent emotion recognition is integral to achieve adaptive human-machine interaction (HCI).One preparatory work for emotion recognition is target emotion tagging, a process that assigns proper emotional labels to improve the efficiency of annotation methods of final classification performance [3].Previous pieces of literature have proposed several emotion models. Emotions are known as a group of intrinsic cognitive states of the human mind. It adds meanings to human activities and plays a vital role in human communication, intelligence, and perception [1]. Since emotions are closely associated with human activities and psychophysiological states, establishing intelligent emotion recognition is integral to achieve adaptive human-machine interaction (HCI). One preparatory work for emotion recognition is target emotion tagging, a process that assigns proper emotional labels to improve the efficiency of annotation methods of final classification performance [3]. Previous pieces of literature have proposed several emotion models. Some of them, such as Ekman’s and Parrot’s, are widely adopted but are poor in the term of the number of emotions (six emotions)

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