Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease
This work addresses Alzheimer's disease diagnosis, but it is incremental as it focuses on electrode density impact without introducing new methods.
The study tackled the problem of classifying Alzheimer's disease patients from healthy controls using TMS-EEG responses, achieving a best accuracy of 92.7% with a high-density electrode setup and Random Forest classifier.
Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD). In this work, we investigate the use of TMS-evoked EEG responses to classify AD patients from healthy controls (HC). By using a dataset containing 17AD and 17HC, we extract various time domain features from individual TMS responses and average them over a low, medium and high density EEG electrode set. Within a leave-one-subject-out validation scenario, the best classification performance for AD vs. HC was obtained using a high-density electrode with a Random Forest classifier. The accuracy, sensitivity and specificity were of 92.7%, 96.58% and 88.2% respectively.