EEG-based emotion recognition with autoencoder feature fusion and MSC-TimesNet model

Electroencephalography (EEG) signals are widely employed due to their spontaneity and robustness against artifacts in emotion recognition. However, existing methods are often unable to fully integrate high-dimensional features and capture changing patterns in time series when processing EEG signals, which results in limited classification performance. This paper proposes an emotion recognition method (AEF-DL) based on autoencoder fusion features and MSC-TimesNet models. Firstly, we segment the EEG signal in five frequency bands into time windows of 0.5 s, extract power spectral density (PSD) features and differential entropy (DE) features, and implement feature fusion using the autoencoder to enhance feature representation. Based on the TimesNet model and incorporating the multi-scale convolutional kernels, this paper proposes an innovative deep learning model (MSC-TimesNet) for processing fused features. MSC-TimesNet efficiently extracts inter-period and intra-period information. To validate the performance of the proposed method, we conducted systematic experiments on the public datasets DEAP and Dreamer. In dependent experiments with subjects, the classification accuracies reached 98.97% and 95.71%, respectively; in independent experiments with subjects, the accuracies reached 97.23% and 92.95%, respectively. These results demonstrate that the proposed method exhibits significant advantages over existing methods, highlighting its effectiveness and broad applicability in emotion recognition tasks.