Emotion recognition from EEG signals is an area of increasing interest due to its potential applications in healthcare, human-computer interaction, and mental health monitoring. Electroencephalography signals reflect the brain’s electrical activity and offer valuable insights into emotional states. Recognizing emotions from EEG signals, however, is a challenging task due to the complexity and variability of brain activity. Traditional emotion recognition techniques often struggle to capture the intricate patterns in EEG data that are associated with different emotional states. The development of more sophisticated models, such as those based on attention mechanisms, holds promise in improving the accuracy and robustness of emotion recognition systems. This report explores the application of a dual attention mechanism to enhance the performance of EEG signal emotion recognition models. The dual attention mechanism in the context of EEG signal emotion recognition involves the use of two distinct attention mechanisms to focus on relevant features in the EEG data. Attention mechanisms, which are inspired by human cognition, allow models to prioritize important input features and suppress irrelevant ones. In the case of EEG signals, attention mechanisms can help the model focus on specific temporal and spatial patterns in the data that are most indicative of emotional states.
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