A Hybrid Deep Learning Framework for EEG-Based Emotion Recognition Using ResNet50 and LSTM Architectures

Abstract

Emotion and feeling recognition by analyzing Electroencephalogram (EEG) signal consider a huge leap in the development in the aspects of human-computer interaction and affective computing. This paper suggests an innovative hybrid method that combines Residual Network (ResNet50) and Long Short-Term Memory (LSTM) deep learning architectures for emotion classification from EEG signals. Our methodology utilizes the spatial feature extraction by ResNet50 with the temporal sequence modeling strengths of LSTM networks. The proposed method was tested and validated on a complete dataset consists of EEG recordings from 28 subjects during emotionally-exciting computer game sessions, achieving a very well performance metrics that reaching approximately 0.99. Comparative study shows and illustrated that our framework or approach outperforms individual models or networks, considered as a new ideal system for EEG-based emotion recognition. This research consider a noticeable achievement add to the aspects of affective computing and the related aspect such as human-computer interaction and many other potential applications.