Independent component analysis of oddball EEG recordings to detect Parkinson's disease.

Parkinson's Disease (PD) is one of the most common diseases affecting the human brain, thus approaches are needed to help diagnose it. Since the changes caused by PD are visible in electroencephalograms (EEG), analysis of EEG represents one such approach. In this study, we used 25 EEG recordings of PD patients and 25 of healthy controls, subjected to auditory tasks, available in the Parkinson's Oddball database. The mean age of the PD patients was 69.7 years (std. 8.7) and 69.3 years (std. 9.6) of the control subjects. We employed the Independent Component Analysis (ICA) method to characterize the PD and control EEG recordings, to represent the changes in habituation as a response to different auditory events via the ICA components in the form of topological distributions, and to classify the EEG recordings of the two groups. Characterization of the frontal and central electrodes of the topological distribution showed high separation power to differentiate EEG recordings of the PD patients and healthy subjects. The average classification results using 5-fold cross-validation over 50 trials and the first four features ranked according to the variance of the ICA components, while the features were logarithm of the variance of the ICA components, yielded the following performances: classification accuracy of 88.56%, sensitivity of 89.36%, and specificity of 87.76%. The use of the ICA method appears to be a promising approach for characterizing and classifying auditory EEG recordings.