Canonical Correlation to Estimate the Degree of Parkinsonism from Local Field Potential and Electroencephalographic Signals

• Type of Material: Conference Paper
• Authors: Teresa H. Sanders, Georgia Institute of Technology; Annaelle Devergnas, Emory University; Thomas Wichmann, Emory University; Mark A. Clements, Georgia Institute of Technology
• Language: English
• Date: 2013-11-06
• Publisher: IEEE
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2013 IEEE.
• Final Published Version (URL): http://dx.doi.org/10.1109/NER.2013.6695896
• Title of Journal or Parent Work: 2013 6TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)
• Conference or Event Name: 6th International IEEE EMBS Conference on Neural Engineering (NER)
• Start Page: 158
• End Page: 161
• Grant/Funding Information: This project was supported through grants from the NIH/NINDS (R01-NS054976 [TW] and P50-NS071669 [TW]), as well as a grant from the NIH to the Yerkes National Primate Research Center (P510-RR000165, now P51-OD011132).; TS was supported by Texas Instruments (TI) under the TI Leadership University (TILU) Fellowship.
• Keywords: THETA; PRIMARY MOTOR CORTEX; Neurosciences; OSCILLATIONS; Technology; Neurosciences & Neurology; Engineering; PALLIDUM; DISEASE; Engineering, Biomedical; SUBTHALAMIC NUCLEUS; BASAL GANGLIA; Science & Technology; Life Sciences & Biomedicine; NETWORKS

Abstract

In this study, modulation index (MI) features derived from local field potential (LFP) recordings in the sub-thalamic nucleus (STN) and electroencephalographic recordings (EEGs) from the primary motor cortex are shown to correlate with both the overall motor impairment and motor subscores in a monkey model of parkinsonism. The MI features used are measures of phase-amplitude cross frequency coupling (CFC) between frequency sub-bands. We used complex wavelet transforms to extract six spectral sub-bands within the 3-60 Hz range from LFP and EEG signals. Using the method of canonical correlation, we show that weighted combinations of the MI features in LFP or EEG signals correlate significantly with individual and composite scores on a scale for parkinsonian disability.