Multi Channel EEG

Multi-channel electroencephalography (EEG) research focuses on extracting meaningful information from the simultaneous recordings of brain electrical activity across multiple scalp locations. Current efforts concentrate on improving signal processing techniques, particularly through deep learning architectures like convolutional neural networks, transformers, and recurrent neural networks, often incorporating attention mechanisms to enhance feature extraction and classification accuracy. These advancements are driving progress in diverse applications, including brain-computer interfaces, diagnosis of neurological disorders (e.g., epilepsy, ADHD), and understanding cognitive processes like emotion and sleep. The ultimate goal is to develop robust and reliable methods for decoding brain activity from multi-channel EEG data, leading to improved clinical tools and a deeper understanding of brain function.