Abstract
Electroencephalograph (EEG) is a window of mind that detects the abnormalities in your brain waves, and EEG measurements are commonly used in different research areas in the field of medicine. The effective and affordable EEG headsets drew the attention of researchers in the field of human–machine system. The preprocessing stage of the EEG signals is important due to the noise present in the signal which is followed by the stages feature extraction and classification. Several filters are used to denoise the signals. Feature extraction and classification are important and useful technologies in medical applications. For early diagnosis of a variety of diseases, acquiring brain signals has become important. EEG signals contain information, and with the help of different feature extraction techniques, useful information and characteristics are acquired. Classification accuracy not only depends on the working of the classifier but also it is about the input EEG signal. Feature extraction is a process applied to get the properties of the signal that makes it different from the signal of the other mental tasks. A result of brain–computer interface system directly depends on the effectiveness of the extracted features and classification done. Several classifiers are used for the classification, and the accuracy of the results varies according to the classifier used.
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Chakole, A.R., Barekar, P.V., Ambulkar, R.V., Kamble, S.D. (2019). Review of EEG Signal Classification. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 107. Springer, Singapore. https://doi.org/10.1007/978-981-13-1747-7_11
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DOI: https://doi.org/10.1007/978-981-13-1747-7_11
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