Application of Adaptive Chebyshev and Fast-Fourier Transform to Identify Bradykinesia in Humans from EEG Data Features
Keywords:Adaptive Chebyshev, Bradykinesia, Discrete Time - Direct Form Filter, Electroencephalogram (EEG), Fast-Fourier Transform, MATLAB, Neuroscience, Signal Processing
Neuroscience is a field that requires utmost meticulousness, and careful dissection of the signals involved. Brain disorders are diverse in nature, and entails one to hold a holistic cognizance of the structure and working of the neurons, along with its signals involved. The Electroencephalogram (EEG) is a data that aids in agnizing the abnormalities, and therefore requires scrupulous analysis through appropriate techniques. Bradykinesia is a neurological disorder which can subsequently lead to Parkinson’s disease, and thus requires explicit observation of any small differences at its early stages through the EEG feature signals. While algorithmic approaches from previous studies have entailed the movements and development of sensory organs, this study pivots on recognizing the abnormality of the EEG data signal for an individual through pre-processing and signal processing methods. This paper pivots on utilizing Fast-Fourier Transform (FFT) and Adaptive Chebyshev using Discrete time-direct form filter to segregate the spectrum bands, the Power Spectral Density (PSD), along with the process of side lobe reduction are proposed in order to unambiguously comprehend the inconsistencies of the frequency amplitude between the normal and abnormal signals. The simulations for this study are carried out in MATLAB, and the results are successfully obtained.
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