Deep learning for electroencephalography emotion recognition

This study presents an Electroencephalography (EEG) emotion recognition using a long short-term memory (LSTM)-based method. Our proposed method selects window sizes and overlaps to divide the EEG data into segments, which optimally captures subtle signal changes. A Bidirectional LSTM (BiLSTM) layer is added to standard LSTM layers to better detect forward and backward patterns in the data. By using this dual-layer setup, we aim to improve both the feature extraction and the classification accuracy. The model was tested on the Database for Emotion Analysis using Physiological signals (DEAP) dataset and showed acceptable accuracy across emotional dimensions: arousal (94.0%), liking (98.9%), dominance (95.3%), and valence (99.6%). Our results suggest that the model better supports emotion recognition and has potential for mental health monitoring and adaptive therapy.