EEG Brain Emotion Classification Using AI

Emotion recognition from EEG brain signals is a challenging yet crucial task in affective computing and neuro informatics. In paper, we file a DNN-based approach for automatic emotion classification using EEG data. Our method utilizes a multilayered architecture comprising CNNs and RNNs to absorb ranked representations from raw EEG signals. We explore various preprocessing techniques to enhance the discriminative power of the model and employ transfer learning strategies to hold instructed neural network models for feature extraction. The framework is resulted on standard datasets, demonstrating promising results in accurately classifying emotional states. Our findings highlight the potential of deep neural networks in grasping difficult patterns in data and the field of affective computing towards real-world applications in mental health monitoring and humancomputer interaction.