A Review on Topics where Machine Learning has been used to Process EEG Signals

Abstract

The last decade has seen an increase in the use of artificial intelligence (AI) and machine learning (ML). Recent advances in the field of BC have led to renewed interest in the use of electroencephalography (EEG) for different fields. EEG is used in medical and biomedical applications such as analyzing mental workload and fatigue, diagnosing brain tumors and rehabilitation of central nervous system disorders; EEG-based motion analysis and classification is widely used in many areas from clinical applications to brain-machine interface and robotic applications. This article reviews the applications of many ML algorithms used in EEG signal processing, introduces commonly used algorithms, typical application scenarios, important advances and current problems. The study explored current applications of ML in EEG, including brain-computer interfaces, cognitive neuroscience, diagnosis of brain disorders, and more. First, the basic principles of ML algorithms used in EEG signal processing, including convolutional neural network, support vector machines, K-nearest neighbor and multidirectional convolutional neural network, are briefly explained. In addition, a general research on ML applications used in EEG analysis is presented. As a result, it was determined that the most SVM and CNN methods were used in the studies, and the study titles were mainly on epilepsy, BCI and Emotion, and the least on Alcohol, Sleeping States, Perception.