Electroencephalography Recording

Electroencephalography (EEG) recording measures brain electrical activity to understand various cognitive and neurological processes. Current research heavily utilizes deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs and GRUs), and transformers, often combined with attention mechanisms, to analyze EEG data for applications like emotion recognition, sleep stage classification, and disease diagnosis (e.g., Alzheimer's, Parkinson's, epilepsy, ADHD). These advancements are improving the accuracy and efficiency of EEG-based diagnostics and enabling the development of novel brain-computer interfaces and personalized interventions. The field is also focusing on improving model interpretability and addressing challenges related to data variability across subjects and the need for larger, more diverse datasets.