Emotion Recognition of EEG Signals Based on the Ensemble Learning Method: AdaBoost

Jifeng Guo,Yu Chen,Jerzy Baranowski,Rui Chang

doi:10.1155/2021/8896062

Jifeng Guo, Yu Chen + Show 2 more

Open Access

https://doi.org/10.1155/2021/8896062

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Abstract

In recent years, with the continuous development of artificial intelligence and brain-computer interface technology, emotion recognition based on physiological signals, especially, electroencephalogram (EEG) signals, has become a popular research topic and attracted wide attention. However, how to extract effective features from EEG signals and accurately recognize them by classifiers have also become an increasingly important task. Therefore, in this paper, we propose an emotion recognition method of EEG signals based on the ensemble learning method, AdaBoost. First, we consider the time domain, time-frequency domain, and nonlinear features related to emotion, extract them from the preprocessed EEG signals, and fuse the features into an eigenvector matrix. Then, the linear discriminant analysis feature selection method is used to reduce the dimensionality of the features. Next, we use the optimized feature sets and train a classifier based on the ensemble learning method, AdaBoost, for binary classification. Finally, the proposed method has been tested in the DEAP data set on four emotional dimensions: valence, arousal, dominance, and liking. The proposed method is proved to be effective in emotion recognition, and the best average accuracy rate can reach up to 88.70% on the dominance dimension. Compared with other existing methods, the performance of the proposed method is significantly improved.

Highlights

Emotion plays an important role in people’s social activities
With the continuous development of artificial intelligence and brain-computer interface technology [12, 13], emotion recognition based on physiological signals, especially on electroencephalogram (EEG) signals, has gradually become the mainstream of emotion recognition [14, 15]
The proposed method has been tested in the DEAP data set on four emotional dimensions: valence, arousal, dominance, and liking. e proposed method is proved to be effective in emotion recognition, and the best average accuracy rate can reach up to 88.70% on the dominance dimension

Summary

Introduction

Emotion plays an important role in people’s social activities. It is the carrier of nonverbal communication between people and makes our daily life vivid. E idea of emotion recognition research based on EEG signals can be summarized as data preprocessing, feature extraction, classification, and evaluation of the model’s performance [15]. Based on feature extraction and classification with machine learning, many research methods were proposed and promoted the progress of emotion recognition of EEG. In recent years, in addition to traditional machine learning and deep learning methods, the classification method based on ensemble learning [25, 26] has gradually attracted the attention of many researchers and achieved good results in the emotion recognition of EEG signals. We use the optimized feature sets and train a classifier based on the ensemble learning method, AdaBoost, for binary classification. The proposed method has been tested in the DEAP data set on four emotional dimensions: valence, arousal, dominance, and liking. More details of each step of the route are as follows

DEAP Data Set

Results

Hjorth parameter-complexity:

Wavelet entropy

Findings

Conclusion