Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease

TL;DR

This study explores how EEG electrode density affects the accuracy of classifying Alzheimer's disease using TMS-EEG responses, demonstrating high-density EEG improves diagnostic performance.

Contribution

It introduces a comparison of EEG density levels in TMS-EEG classification of AD, highlighting the effectiveness of high-density EEG with machine learning.

Findings

High-density EEG yields 92.7% accuracy in AD classification.

Random Forest classifier performs best with high-density EEG.

High-density EEG improves sensitivity and specificity in AD detection.

Abstract

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.