Brain Signal-Based Emotion Classification Using DEAP Corpus

The research paper presents a study on utilizing deep learning models, particularly Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), to classify emotions based on Electroencephalography (EEG) signals captured from the human brain using electrodes placed on the scalp. The study primarily analyses the DeapData corpus, employing advanced deep-learning techniques to interpret and classify emotional states from EEG readings. The LSTM model is utilized for its capability to capture long-term dependencies in sequential data, while the CNN model is harnessed for its effectiveness in extracting features from EEG brain signals. Experimental results demonstrate the efficiency of the proposed approach in accurately categorizing emotions from EEG brain signal data, emphasizing the potential of deep learning techniques in neuroimaging and affective computing applications.