EEG Biomarkers

EEG biomarkers are measurable patterns in brainwave activity used to identify and monitor neurological and psychological conditions. Current research focuses on developing robust methods for extracting these biomarkers, employing machine learning techniques like deep neural networks (e.g., CNN-LSTMs, SE-ResNets) and self-supervised learning to improve accuracy and efficiency across diverse datasets. This work is significant for advancing diagnostic capabilities, particularly in areas like Parkinson's disease and sleep disorders, and for creating more effective brain-computer interfaces by enhancing the reliability of EEG signals. Furthermore, research is addressing challenges such as data variability and improving the accessibility of EEG-based diagnostics through the use of alternative signals like EOG.