DEEP LEARNING ARCHITECTURES FOR EXPLORING SPATIO-TEMPORAL PATTERNS FROM EEG DATA FOR EMOTION DETECTION

Human emotions are crucial across various domains by closing the divide between human and current technologies, thus fostering better understanding on critical mental health conditions. Traditional approaches rely mainly on facial expressions along with body gestures and are met with limited success as they miss underlying or repressed emotions. Electro Encephalo Gram (EEG) signals offer a direct glimpse into brain activity, making them a promising avenue for fool-proof emotion recognition. However, the complex temporal dynamics of EEG data pose challenges for classical machine learning algorithms, which often fail to capture spatial and temporal patterns effectively. To overcome these issues, we introduced a novel model called Spatio-Temporal Difference Identification - Convolution Neural Network (STDI-CNN). Our model efficiently captures the complex temporal dynamics of EEG data, indicating brain activity across time in various lobes, by utilizing deep learning approaches, especially a combination of CNN and sequential neural network architectures. Extensive experiments on the SEED EEG dataset demonstrate the efficacy of the proposed STDI-CNN model, achieving an impressive accuracy of 98.52%. Additional tests using CNN-LSTM and CNN-BiLSTM models also yielded strong performance, with accuracy rates of 97.04%. This surpasses current SOA models and highlights the potential of STDI-CNN in extracting meaningful patterns from EEG signals for emotion recognition. Our work reduces the gap by featuring a significant step forward in harnessing EEG signals to build well informed emotionally intelligent system that fosters prior detection and improved diagnosis for neurological disorders.