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

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.

A method for identifying moonlighting proteins based on linear discriminant analysis and bagging-SVM

“…Linear discriminant analysis (LDA) is a feature selection technique ( Arjmandi and Pooyan, 2012 ; Xie et al, 2018 ; Yang et al, 2020 ; Chen Y. et al, 2021 ). It can effectively reduce the feature dimension and reduce the error caused by redundant data.…”

EEG Based Emotion Recognition: A Tutorial and Review

“…Hence, multiple types of higher-level features can be extracted from those subnetworks that promote the generalization capability and the robustness against data imbalance. Chen et al [208] proposed to apply the Adaboost algorithm to elevate the recognition performance adaptively. As shown in Figure 29, it works based on the iteration mechanism.…”

E2ENNet: An end-to-end neural network for emotional brain-computer interface

“…In traditional EEG emotion recogniton process, feature extraction is a vital procedure. As shown in Figure 1, after preprocessing the EEG signals, usually it is necessary to extract features from raw EEG signals, then input them into the network for classification and recognition (Duan et al, 2013;Chen et al, 2021;Ma et al, 2021). Duan et al (2013) proposed the differential entropy (DE) feature of five frequency bands and obtained satisfactory classification results using DE features.…”

“…Hao et al (2021) proposed a lightweight convolutional neural network that extracts PSD features as input and conducted experiments on the DEAP dataset, which attained 82.33 and 75.46% for Valance and Arousal, respectively. Chen et al (2021) proposed an integrated capsule convolution neural network (CapsNet), which used Wavelet packet transform (WPT) for feature extraction. The average On the other hand, many deep learning methods need not to extract features manually while run end-to-end.…”

“…Bold values represent the lowest time cost and the highest accuracy.environment. As shown in Table5, we compared the training time, testing time, and accuracy of SVM, DBN, CapsNet(Chen et al, 2021) and three ablation models mentioned in Section 3.4.2 on the DEAP dataset. We can see that SVM is the least time-consuming method but the accuracy is not very good, the testing time of DBN is too long.…”