Interpretable Cross-Subject EEG-Based Emotion Recognition Using Channel-Wise Features

Abstract

Electroencephalogram (EEG)-based emotion recognition is receiving significant attention in research on brain-computer interfaces (BCI) and health care. To recognize cross-subject emotion from EEG data accurately, a technique capable of finding an effective representation robust to the subject-specific variability associated with EEG data collection processes is necessary. In this paper, a new method to predict cross-subject emotion using time-series analysis and spatial correlation is proposed. To represent the spatial connectivity between brain regions, a channel-wise feature is proposed, which can effectively handle the correlation between all channels. The channel-wise feature is defined by a symmetric matrix, the elements of which are calculated by the Pearson correlation coefficient between two-pair channels capable of complementarily handling subject-specific variability. The channel-wise features are then fed to two-layer stacked long short-term memory (LSTM), which can extract temporal features and learn an emotional model. Extensive experiments on two publicly available datasets, the Dataset for Emotion Analysis using Physiological Signals (DEAP) and the SJTU (Shanghai Jiao Tong University) Emotion EEG Dataset (SEED), demonstrate the effectiveness of the combined use of channel-wise features and LSTM. Experimental results achieve state-of-the-art classification rates of 98.93% and 99.10% during the two-class classification of valence and arousal in DEAP, respectively, with an accuracy of 99.63% during three-class classification in SEED.

Accurate emotion recognition using Bayesian model based EEG sources as dynamic graph convolutional neural network nodes

“…The lowest accuracy in this comparison is related to the method in 11 with 67.7%. However, for the method 26 , the average accuracy is 96.87%. It can be seen that in all subjects, the highest accuracy is related to the proposed method with 98.95%.…”

“…The accuracy of the subject-dependent scenario of the proposed method and the existing methods 11 , 12 , 17 , 21 , 23 , 25 , 26 are compared in Fig. 9 .…”

“…It can be seen that in all subjects, the highest accuracy is related to the proposed method with 98.95%. The proposed method and the methods 11 , 12 , 17 , 21 , 23 , 25 , 26 in Fig. 10 were compared in the form of subject-independent scenario.…”

“…Accuracy of 73.84% on the SEED-IV dataset is achieved and 85.30% on EEG SEED dataset for this procedure. In 26 , channel-wise features are applied as the input of two-layer stacked short-term memory (LSTM). Accuracies of 98.93% and 99.10% during the two-class classification of valence and arousal in the DEAP dataset and an accuracy of 99.63% during the three-class classification on the SEED dataset have been attained, respectively.…”

Effect of a Brain–Computer Interface Based on Pedaling Motor Imagery on Cortical Excitability and Connectivity

“…Thus, this information may contribute to developing more effective therapy with BCIs by obtaining a classification model based on connectivity from various subjects. In fact, a subject independent BCI based on brain region connectivity was recently proposed for emotion recognition, achieving promising results [ 38 ].…”