Deep Learning Methods in Electroencephalography

The volume, variability and high level of noise in electroencephalographic (EEG) recordings of the electrical brain activity make them difficult to approach with standard machine learning techniques. Deep learning methods, especially artificial neural networks inspired by the structure of the brain itself are better suited for the domain because of their end-to-end approach. They have already shown outstanding performance in computer vision and they are increasingly popular in the EEG domain. In this chapter, the state-of-the-art architectures and approaches to classification, segmentation, and enhancement of EEG recordings are described in applications to brain-computer interfaces, medical diagnostics and emotion recognition. In the experimental part, the complete pipeline of deep learning for EEG is presented on the example of the detection of erroneous responses in the Eriksen flanker task with results showing advantages over a traditional machine learning approach. Additionally, the refined list of public EEG data sources suitable for deep learning and guidelines for future applications are given.