Abstract
Biomedical signal analysis has a great demand for effective clinical and hospital services. Emerging techniques need to be developed and applied for diagnosis and treatment of the patients. Simultaneously it will be the better support to the physicians. In current trend, processing is likely to be digital. Physiological signals like ECG, EMG, EEG, and imaging like CT, MRI are to be well analyzed for better accuracy, detection, and diagnosis. The research related to biosignals increases exponentially. Electroencephalograph (EEG) is one of these signals and has a vital role in the study of brain activity, as well as different brain-related diseases, disorders, and treatments in the field of medicine. This chapter aims to application of machine learning techniques for electroencephalogram (EEG) analysis as varieties of brain disorders are diagnosed by visual inspection of EEG signals. Initial phase provides the basics of EEG, acquisition, and necessity of analysis. Next to it, different techniques used earlier including computational intelligence are provided. Further, use of deep neural networks as an emerging intelligent technique is provided for different modes of EEG analysis by researchers. Finally, an example of classification is depicted with the future prospects.
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Mohanty, M.N. (2022). Cognitive Techniques for Brain Disorder Management: A Future Trend. In: Mishra, S., Tripathy, H.K., Mallick, P., Shaalan, K. (eds) Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis. Studies in Computational Intelligence, vol 1024. Springer, Singapore. https://doi.org/10.1007/978-981-19-1076-0_15
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