Electroencephalogram Data

Electroencephalogram (EEG) data, representing the brain's electrical activity, is increasingly used to diagnose and monitor neurological conditions and understand cognitive processes. Current research focuses on applying advanced machine learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs), and attention-based architectures, to analyze EEG signals for improved diagnostic accuracy in conditions like Alzheimer's and Parkinson's diseases, as well as for applications in emotion recognition, gaze estimation, and sleep apnea detection. These efforts aim to improve the speed, accuracy, and objectivity of diagnoses, leading to earlier interventions and better patient outcomes, while also advancing our understanding of brain function.