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ELECTROENCEPHALOGRAM CLASSIFICATION, ARTIFICIAL INTELLIGENCE, DEEP LEARNING, ROBOTICS, COMPUTER SCIENCE, MACHINE LEARNING, BRAIN, DATA, NEURO SCIENCE, SOCIOLOGY
The oldest diagnostic method in the field of neurology is electroencephalography (EEG). To grasp the information contained in EEG signals, numerous deep machine learning architectures have been developed recently. In brain computer interface (BCI) systems, classification is crucial. Many recent studies have effectively employed deep learning algorithms to learn features and classify various sorts of data. A systematic review of EEG classification using deep learning was conducted in this research, resulting in 90 studies being discovered from the Web of Science and PubMed databases. Researchers looked at a variety of factors in these studies, including the task type, EEG pre-processing techniques, input type, and the depth of learning. This study summarises the current methodologies and performance results in EEG categorization using deep learning. A series of practical recommendations is provided in the hopes of encouraging or directing future research using EEG datasets to use deep learning.
Patel, Hrishitva, "Electroencephalogram classification of brain states using deep learning approach" (2022). Computer Science Faculty Scholarship. 7.
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