Time Series Analysis Applied to EEG Shows Increased Global Connectivity during Motor Activation Detected in PD Patients Compared to Controls

Abstract

Background: Brain connectivity has shown to be a key characteristic in the study of both Parkinson’s Disease (PD) and the response of the patients to the dopaminergic medication. Time series analysis has been used here for the first time to study brain connectivity changes during motor activation in PD. Methods: A 64-channel EEG signal was registered during unilateral motor activation and resting-state in 6 non-demented PD patients before and after the administration of levodopa and in 6 matched healthy controls. Spectral entropy correlation, coherence, and interhemispheric divergence differences among PD patients and controls were analyzed under the assumption of stationarity of the time series. Results: During the motor activation test, PD patients showed an increased correlation coefficient (both hands p < 0.001) and a remarkable increase in coherence in all frequency range compared to the generalized reduction observed in controls (both hands p < 0.001). The Kullback­–Leibler Divergence (KLD) of the Spectral Entropy between brain hemispheres was observed to increase in controls (right hand p = 0.01; left hand p = 0.015) and to decrease in PD patients (right hand p = 0.02; left hand p = 0.002) with motor activation. Conclusions: Our results suggest that the oscillatory activity of the different cortex areas within healthy brains is relatively independent of the rest. PD brains exhibit a stronger connectivity which grows during motor activation. The levodopa mitigates this anomalous performance.

Parkinson’s Detection: Emotional Intelligence Perspective

“…Several approaches are utilised to examine EEG data in Electroencephalography (EEG) Analysis for Parkinson's disease identification., including (I)Power spectral density analysis: This method involves calculating the power spectral density of the EEG signals to identify changes in the frequency distribution of the signals, which are associated with Parkinson's disease[63]. (II)Time-frequency analysis: This method involves analysing the time-frequency representation of the EEG signals to identify changes in the frequency content of the signals over time[64]. (III)Independent This method involves separating the EEG signals into independent components and analysing the properties of each component to identify patterns that are indicative of Parkinson's disease[65].…”