title: 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.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Weyhenmeyer
authors.forename: Manuel E
authors.initials: ME
authors.lastname: Hernandez
authors.forename: Claudia
authors.initials: C
authors.lastname: Lainscsek
authors.forename: Terrence J
authors.initials: TJ
authors.lastname: Sejnowski
authors.forename: Howard
authors.initials: H
authors.lastname: Poizner
mesh_terms: D000368:Aged; D016477:Artifacts; D001479:Basal Ganglia / Q000502:physiology; D016022:Case-Control Studies; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000188:drug therapy / Q000503:physiopathology*; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted