Recognition of Normalization Grounded Adaptive Filtering Methods For Noise Cancellation In EEG Signal Recordings

Electroencephalograms (EEGs) are promising tools for the diagnosis and treatment of neurological and mental disorders, and they are gaining ground as a measurement of brain activity. The electrical activity of the brain can be measured and recorded with an electroencephalogram (EEG). Electrodes are usually placed along the scalp and wired to a recorder to convey the data. The brain and spinal cord together make up the central nervous system, which controls every function in the human body. It receives data from the sensory organs, analyses and organizes it, and then decides what actions to direct the rest of the body to carry out based on that data. When a human brain is damaged, it can have catastrophic effects on every area of that person's existence. Accidents, assaults, concussions, lack of oxygen (near drowning), Alzheimer's disease and other degenerative diseases (dementia, Parkinson's disease), alcohol and other drugs, brain tumors, epilepsy, seizures, infections, and diseases (meningitis, encephalitis) are all examples of what are considered brain disorders. The goal is to design a variety of innovative ANC's for EEG signal denoising that address the challenges of computational complexity, Signal-to-Noise Ratio, Mis-regulation, and convergence. This is done by taking into account three types of adaptive algorithms: Adaptive algorithms with normalization.