ABSTRACT
This chapter proposes a method to detect one of the neurodegenerative disorder, namely Parkinson’s Disease (PD) faster using the cross-correlation and Independent Component Analysis for electroencephalogram (EEG) channels, which is used to find time delays at scalp level to serve as a metric for distinguishing study groups. Furthermore, the chapter provides deep insight on how the electric dipole evoked by neurons can help in PD diagnosis. This chapter also provides insights on the spectral analysis of different EEG frequency components (i.e. Alpha, Beta, Gamma, Delta) and how they vary majorly in between PD and normal subjects. These components can be widely used for subjective study and detection of PD symptoms and faster diagnosis. The chapter aims to provide a comprehensive assessment of PD and to provide an algorithm and methodology for PD gait, tremor, and unbalance prevention. State-of-art PD treatment procedures are highlighted using EEG scans and DBS signal charts. The applied method helps in detecting an early PD activity at scalp level in right temporal, frontal, parieto-occipital, and occipital regions with greater accuracy. It proposes a future methodology that can be taken to prevent tremors and how to build an assistive device for PD with the help of inertial sensors and nano strain gauges.
