Attention-based model for smart EEG Categorization

Deep learning advancements are focusing on attentional mechanisms for graph analysis to pinpoint the crucial elements for improved decision-making. By using attentional mechanisms, the network can improve the accuracy of the classification task by focusing on the most important parts of the input signals. Functional connectivity of the human brain is a complex field requiring expert clinical knowledge due to the swift alterations in bodily functions, anomalies, and significant differences between individuals often present in EEG signals. These factors make it challenging to accurately classify EEG signals as normal or abnormal. However, the classification task can be made more manageable by using brain network representation of EEG signals. Brain network representation allows for the creation of a graph-based model of the functional connectivity of the human brain, which can be used to recognize important patterns and features in the EEG signals that are relevant to specific brain activities or conditions. The aim is to assess the efficacy of attentional mechanisms in a crucial scenario by examining the brain network representation of EEG signals recorded during a task that involves distinguishing between normal and abnormal EEG patterns.