Emotion recognition from multichannel EEG signals based on low-rank subspace self-representation features

In recent years, emotion recognition based on electroencephalogram (EEG) has become the research focus in human-computer interaction (HCI), but deficiencies in EEG feature extraction and noise suppression are still challenging. In this paper, a novel robust low-rank subspace self-representation (RLSR) of EEG is developed for emotion recognition. Instead of using classical time-frequency EEG feature, the data-driven based EEG self- representation in low-rank subspace is extracted for emotion characterization. The Robust Principal Component Analysis (RPCA) is incorporated to separate the noise part in the process of solving self-representation. The accuracy and robustness of the result are improved because of the superior features and noise suppression. To fully exploit the effective knowledge of different EEG frequency bands, the Tucker decomposition based data dimensionality reduction is introduced. Experiments conducted on the public dataset DEAP reveal that the average accuracies of the proposed method can reach to 93.04% and 93.13% for binary classification of valence and arousal, respectively. The average accuracy reaches to 88.82 % of four-class classification.