An Improved EEG-Based LRCN Emotion Recognition System Using Fuzzy Processing on ECG and PPG Features

In this paper, we proposed an improved 3 classes emotion classification system based on a Long-Term Recurrent Convolutional Network (LRCN) model using Electroencephalogram (EEG) signals, reinforced with a fuzzification process on extracted Electrocardiogram (ECG) and Photoplethysmogram (PPG) features. Although a good average accuracy can be achieved at 75%, the accuracy for some specific subjects remained very poor, mostly caused by a low correlation between EEG signal and emotion in a particular subject, or by a lowquality EEG signal recording. The fuzzification process on extra physiological signals was added for EEG-based LRCN to improve the total average accuracy by 8% and correct some low correlated EEG signals and emotions for certain subjects.