Research on Channel Selection and Multi-Feature Fusion of EEG Signals for Mental Fatigue Detection.

Lei Wang,Yang Liu,Kun Chen,Quan Liu,Qingsong Ai,Zhilei Li,Li Ma

doi:10.3390/e23040457

Abstract

With the rapid development of modern social science and technology, the pace of life is getting faster, and brain fatigue has become a sub-health state that seriously affects the normal life of people. Electroencephalogram (EEG) signals reflect changes in the central nervous system. Using EEG signals to assess mental fatigue is a research hotspot in related fields. Most existing fatigue detection methods are time-consuming or don’t achieve satisfactory results due to insufficient features extracted from EEG signals. In this paper, a 2-back task is designed to induce fatigue. The weight value of each channel under a single feature is calculated by ReliefF algorithm. The classification accuracy of each channel under the corresponding features is analyzed. The classification accuracy of each single channel is combined to perform weighted summation to obtain the weight value of each channel. The first half channels sorted in descending order based on the weight value is chosen as the common channels. Multi-features in frequency and time domains are extracted from the common channel data, and the sparse representation method is used to perform feature fusion to obtain sparse fused features. Finally, the SRDA classifier is used to detect the fatigue state. Experimental results show that the proposed methods in our work effectively reduce the number of channels for computation and also improve the mental fatigue detection accuracy.

Highlights

With rapid economic development, people’s life rhythm is getting faster and faster, and the pressure of competition is becoming greater and greater
To solve the problem of complicated calculation induced by channel redundancy when detecting mental fatigue states with multi-channel EEG data, this paper proposes a common channel selection method based on ReliefF algorithm [21]
Considering that the single feature analysis causes the loss of effective information, and multiple feature concatenation causes high dimension of the feature set, this paper suggests a multi-feature fusion method based on sparse representation to improve the classification effect of fatigue state

Summary

Introduction

People’s life rhythm is getting faster and faster, and the pressure of competition is becoming greater and greater. Research on brain fatigue detection is not limited to improving the accuracy of fatigue state recognition and begins to focus on the combination of brain fatigue detection and practical application This leads to high requirements on the real-time nature of the mental fatigue detection system, the portability of the acquisition equipment, and the complexity of the operation. To solve the problem of complicated calculation induced by channel redundancy when detecting mental fatigue states with multi-channel EEG data, this paper proposes a common channel selection method based on ReliefF algorithm [21]. Considering that the single feature analysis causes the loss of effective information, and multiple feature concatenation causes high dimension of the feature set, this paper suggests a multi-feature fusion method based on sparse representation to improve the classification effect of fatigue state

Channel Selection Based on ReliefF

1: Input: Features

Others

Common Channel Selection Based on Weight Addition m

Sparse Representation

Multi-Class Feature Fusion Based on K-SVD

Subject and Experiment

Procedures

Discussion

Feature Fusion Result Analysis

Conclusions