Deep Learning Methods for EEG Neural Classification

Abstract

Classification of patterns of brain activity in neuroengineering research is an important tool for understanding the brain, developing neurodiagnostics, and designing closed-loop neural interfaces. Scalp electroencephalography (EEG), by virtue of its noninvasiveness and lower cost, has been used for neural signal classification, and researchers have utilized various machine learning methods. Recently, deep learning has gained popularity due to its ability to significantly increase the classification performance in numerous domains while elucidating the relevant features for classification. It is a natural step to deploy such promising techniques for EEG classification tasks. This book chapter aims to serve as a comprehensive reference source for both EEG and deep learning researchers interested in EEG-based deep learning studies. Potential pitfalls, challenges, and opportunities in the application of deep learning to EEG data are discussed.