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

Alexandra-Maria Tautan; Elias Casula; Ilaria Borghi; Michele Maiella; Sonia Bonni; Marilena Minei; Martina Assogna; Bogdan Ionescu; Giacomo Koch; Emiliano Santarnecchi

arXiv · 2206.07492

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

Alexandra-Maria Tautan·Elias Casula·Ilaria Borghi·Michele Maiella·Sonia Bonni·Marilena Minei·Martina Assogna·Bogdan Ionescu·Giacomo Koch·Emiliano Santarnecchi

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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.

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Alexandra-Maria Tautan, Elias Casula, Ilaria Borghi, Michele Maiella, Sonia Bonni, Marilena Minei, Martina Assogna, Bogdan Ionescu, Giacomo Koch, Emiliano Santarnecchi. 2022-05-19. Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease. https://arxiv.org/abs/2206.07492

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