Abstract
The human brain plays a significant role in controlling the behavior of the human body with respect to sensory stimuli, external/internal motor stimuli, and so on. EEG signals are informative signals that contain knowledge about the condition of the brain. They are nonstationary and nonlinear in nature and so, it is hard to detect the subtle yet significant changes in the signals using just the human eye. This gives engineers the opportunity to apply numerous algorithms for the detection of such subtle yet significant changes in EEG signals. Various algorithms have been developed and are being used to extract the important features from EEG signals. It has been established through research that the nonlinear features of EEG mostly comprise the abrupt transitions and its chaotic and random behavior. In this chapter, the effect of EEG signals on diagnosing 6 brain disorders, namely, epilepsy, autism, Alzheimer's disease, Parkinson's disease, schizophrenia, and ischemic brain stroke, is discussed in detail.
Published Version
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