EEG emotion recognition based on linear kernel PCA and XGBoost

Dong Yindong; Ren Fuji; Li Chunbin

doi:10.12086/oee.2021.200013

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Dong Y D, Ren F J, Li C B. EEG emotion recognition based on linear kernel PCA and XGBoost[J]. Opto-Electron Eng, 2021, 48(2): 200013. doi: 10.12086/oee.2021.200013

Citation:

Dong Y D, Ren F J, Li C B. EEG emotion recognition based on linear kernel PCA and XGBoost[J]. Opto-Electron Eng, 2021, 48(2): 200013. doi: 10.12086/oee.2021.200013

EEG emotion recognition based on linear kernel PCA and XGBoost

1.
School of Computer and Information, Hefei University of Technology, Hefei, Anhui 230601, China
2.
Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory, Hefei, Anhui 230601, China
3.
Department of Engineering, the University of Tokushima, Tokushima 770-8509, Japan

Fund Project: National Natural Science Foundation of China-Shenzhen Joint Foundation (Key Project) (U1613217)

Received Date 07 January 2020

Revised Date 11 June 2020

Published Date 15 February 2021

Abstract

Abstract

The principal component analysis of linear kernel and XGBoost models are introduced to design electroencephalogram (EEG) classification algorithm of four emotional states under continuous audio-visual stimulation. In order to reflect universality, the traditional power spectral density (PSD) is used as the feature of EEG signal, and the feature importance measure under the weight index is obtained with XGBoost learning. Then linear kernel principal component analysis is used to process the threshold selected features and send them to XGBoost model for recognition. According to the experimental analysis, gamma-band plays a more important role than other bands in XGBoost model recognition; in addition, for distribution on channels, the central, parietal, and right occipital regions play a more important role than other brain regions. The recognition accuracy of this algorithm is 78.4% and 92.6% respectively under the two recognition schemes of subjects all participation (SAP) and subject single dependent (SSD). Compared with other literature, this algorithm has made a great improvement. The scheme proposed is helpful to improve the recognition performance of brain-computer emotion system under audio-visual stimulation.
- eXtreme gradient boosting /
- linear kernel principal component analysis /
- EEG /
- emotion recognition

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References

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Overview

Overview

Overview: Affective computing aims to build a harmonious human-computer environment so that computers have the ability to recognize and understand emotions. At present, the research of affective computing has penetrated into the fields of face recognition, speech recognition, text representation, gesture expression, and physiological signal. The relevant applications in these fields provide more humanized and emotional interfaces for all levels of human life. As the most direct physiological expression of the central nervous system, EEG contains rich emotional information. Compared with other research fields, the emotional information contained in EEG is more authentic and referential. At the same time, the expression of EEG emotion is not easy to be misled by subjective consciousness. In order to accurately distinguish different emotional states from EEG signals, and combine the corresponding models to explore the emotion-related frequency band and brain area in time and space, the principal component analysis of linear kernel and XGBoost model are introduced to design EEG classification algorithm of four emotional states under continuous audio-visual stimulation in this paper. XGBoost algorithm has the advantages of high speed, low computational complexity, easy parameter adjustment, strong controllability, and high recognition performance. As a recognition and prediction model, XGBoost algorithm has an excellent performance in industry, machine learning, and various scientific research competitions. In addition, XGBoost can measure the importance of features in sample learning according to some feature importance index in the process of sample training, so as to make features more transparent in the process of recognition. First, the traditional power spectral density (PSD) is used as the feature of EEG signal to reflect universality, and the feature importance measure under the weight index is obtained with XGBoost learning. Then the linear kernel principal component analysis is used to increase the dimension of the important features selected by the threshold, which makes the features more nonlinear and separable in the high-dimensional space. Finally, the processed features are sent to XGBoost model for recognition. According to the experimental analysis, gamma-band plays a more important role than other bands in XGBoost model recognition; in addition, for distribution on channels, the central, parietal, and right occipital regions play a more important role than other brain regions. The recognition accuracy of this algorithm is 78.4% and 92.6% respectively under the two recognition schemes of subjects all participation (SAP) and subject single dependent (SSD). Compared with other literature, this algorithm has made a great improvement. Therefore, the scheme proposed is helpful to improve the recognition performance of brain-computer emotion system under audio-visual stimulation.