Electroencephalogram Signal

Electroencephalogram (EEG) signals, reflecting brain electrical activity, are increasingly analyzed to understand various cognitive and neurological states. Current research focuses on applying machine learning, particularly deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs), and capsule networks, to decode EEG data for applications such as sleep apnea detection, seizure prediction, workload assessment, and emotion recognition. These advancements offer improved diagnostic tools and potential for brain-computer interfaces, impacting healthcare and human-machine interaction. The field is also actively exploring methods to address challenges like data scarcity and variability through techniques such as transfer learning and data augmentation.