Identifying Stable Patterns over Time for Emotion Recognition from EEG
This work addresses the challenge of temporal stability in EEG-based emotion recognition for applications in affective computing, though it is incremental as it builds on existing methods with new datasets.
The paper tackled the problem of identifying stable EEG patterns over time for emotion recognition, finding that stable patterns exhibit consistency across sessions and specific neural activations for different emotions, with the system showing relatively stable patterns within and between sessions.
In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. To validate the efficiency of the machine learning algorithms used in this study, we systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset for this study. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotion than negative one in beta and gamma bands; the neural patterns of neutral emotion have higher alpha responses at parietal and occipital sites; and for negative emotion, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition system shows that the neural patterns are relatively stable within and between sessions.