Muscle artifacts in single trial EEG data distinguish patients with Parkinson's disease from healthy individuals

Abstract

Parkinson's disease (PD) is known to lead to marked alterations in cortical-basal ganglia activity that may be amenable to serve as a biomarker for PD diagnosis. Using non-linear delay differential equations (DDE) for classification of PD patients on and off dopaminergic therapy (PD-on, PD-off, respectively) from healthy age-matched controls (CO), we show that 1 second of quasi-resting state clean and raw electroencephalogram (EEG) data can be used to classify CO from PD-on/off based on the area under the receiver operating characteristic curve (AROC). Raw EEG is shown to classify more robustly (AROC=0.59-0.86) than clean EEG data (AROC=0.57-0.72). Decomposition of the raw data into stereotypical and non-stereotypical artifacts provides evidence that increased classification of raw EEG time series originates from muscle artifacts. Thus, non-linear feature extraction and classification of raw EEG data in a low dimensional feature space is a potential biomarker for Parkinson's disease.