EEG Prediction with Deep Recurrent Learning

Electroencephalogram (EEG) reveals the brain dynamics that are of a great value in clinical applications, e.g., epilepsy diagnosis, cognitive status, and other disorders. Intracranial Electroencephalogram (iEEG) is encoding rich dynamics, with its invasive electrodes that go deep into the brain. Despite the high precision, the iEEG surgery may limit its use in daily lives due to the risk, setup time and cost. In this study, we propose a deep learning framework that can reconstruct iEEG from scalp EEG, thereby demonstrating the potential of convenient deep brain monitoring in daily lives. We leverage a long short-term memory (LSTM) model for ultra-deep iEEG prediction from the non-invasive scalp EEG. Our simulation has demonstrated that, our deep learning model, with the nonlinear dynamics mining capability, facilitates the deep source signal estimation performance, compared with the linear model. Furthermore, we find that the estimation performance may be improved with selection of more EEG measurement sites. This study will greatly advance brain dynamics decoding and broadly benefit diverse brain-computer-interface and smart health applications.