This research deals with the recognition of emotions from EEG signals using deep learning based on bi-directional long short-term memory (LSTM) and attention mechanism. In this study, two SEED and DEAP databases are utilized for the emotion recognition. The SEED databas More
This research deals with the recognition of emotions from EEG signals using deep learning based on bi-directional long short-term memory (LSTM) and attention mechanism. In this study, two SEED and DEAP databases are utilized for the emotion recognition. The SEED database includes EEG signals in 62 channels from 15 participants in three categories of positive, neutral, and negative emotions. The DEAP dataset includes EEG signals in 32 channels from 32 participants in two categories of valence and arousal. LSTM has shown its efficiency in extracting temporal information from long physiological signals. The innovations of this research include the use of a new loss function and Bayesian optimizer to find the initial learning rate. The accuracy of the proposed method for the classification of emotions in the SEED database is 96.72%. The accuracy of the proposed method for classifying emotions into two categories of valence and arousal is 94.9% and 97.1%, respectively. Finally, comparing the obtained results with recent research studies.
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