@inproceedings{64ce5a3d54fd4bb29e6d2f62485e96c3,
title = "Toward EEG-Based Brain State Recognition for Personalized Neuromodulation",
abstract = "Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive antidepressant neuromodulation therapy for treatment-resistant depression (TRD). However, the remission rate of patients remains unsatisfactory possibly due to the suboptimal configuration of conventional rTMS protocol. This work aims to design a close-loop TMS system and validate the practicability of brain-state-dependent stimulation based on real-time monitoring of electroencephalogram (EEG). We propose a novel method of phase estimation to enhance the precision of EEG phase-triggered firing of TMS. Our implementation supports subsequent studies on personalized brain-state-dependent neuromodulation for clinical applications.",
keywords = "depression, electroencephalogram (EEG), neural network, Neuromodulation, transcranial magnetic stim-ulation (TMS)",
author = "Chang, {Yu Cheng} and Chao, {Pin Hsuan} and Chen, {Sin Horng} and Chun-Shu Wei",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 15th IEEE International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022 ; Conference date: 19-12-2022 Through 22-12-2022",
year = "2022",
doi = "10.1109/MCSoC57363.2022.00053",
language = "English",
series = "Proceedings - 2022 IEEE 15th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "291--296",
booktitle = "Proceedings - 2022 IEEE 15th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip, MCSoC 2022",
address = "United States",
}