Quantitative EEG-Based Regional Spectral Analysis for Parkinson’s Disease Detection

Parkinson's disease (PD) is a neurological condition marked by progressive motor and cognitive deficits. Quantitative EEG (qEEG) analysis is a non-invasive and cost-effective tool for detecting brain abnormalities associated with PD. This study focusses on using regional spectral analysis of qEEG signals to identify different markers for PD. We investigated power spectral densities and connection patterns in five important brain regions: frontal, central, parietal, temporal, and occipital region using publicly available data from 31 people, including PD patients and healthy controls. The regional irregularities are highlighted using spectral Theta/Alpha Ratio (TAR) obtained by calculating the mean relative power of alpha along with theta band. From the experimental analysis, it can be seen that TAR index is significantly higher (p<0.05) in central (p=0.0259), parietal (p=0.0002), temporal (p =0.0333) and occipital (p=0.00012) lobe of brain in PD patients with on medication state as compared to healthy individuals. Healthy and PD patients off medication showed few differences in parietal and occipital region, whereas a significant difference is reported in central, parietal and occipital region in PD patients in on and off medication states. The results demonstrate that theta/alpha ratio can be used in line with the other neuropsychological assessments to indicate and can act as a potential EEG marker to differentiate between healthy and PD patients.