EEG-based Mouse Cursor Control
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Abstract
The last decade has seen an increase in the use of artificial intelligence (AI) and machine learning. 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 movement analysis and classification is widely used in many areas, from clinical applications to brain-machine interface and robotic applications. This article reviews applications of several BC algorithms used in EEG signal processing, introducing commonly used algorithms, typical application scenarios, key advances, and current problems. The study explored current ML applications in EEG, including brain-computer interfaces, cognitive neuroscience, diagnosis of brain disorders. First, the basic principles of ML algorithms used in EEG signal processing, including convolutional neural networks, support vector machines, K-nearest neighbor, and omnidirectional convolutional neural networks, are briefly described. Additionally, a general survey of BC applications used in EEG analysis is presented. As a result, it was determined that SVM methods were used most in the studies, and the study topics were mainly on epilepsy, BCI, and Emotion, and least on Sleep States and Perception.
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