Projects per year
Abstract
Background: Transcranial magnetic stimulation (TMS) is widely used in brain research and treatment of various brain dysfunctions. However, the optimal way to target stimulation and administer TMS therapies, for example, where and in which electric field direction the stimuli should be given, is yet to be determined. Objective: To develop an automated closed-loop system for adjusting TMS parameters (in this work, the stimulus orientation) online based on TMS-evoked brain activity measured with electroencephalography (EEG). Methods: We developed an automated closed-loop TMS–EEG set-up. In this set-up, the stimulus parameters are electronically adjusted with multi-locus TMS. As a proof of concept, we developed an algorithm that automatically optimizes the stimulation orientation based on single-trial EEG responses. We applied the algorithm to determine the electric field orientation that maximizes the amplitude of the TMS–EEG responses. The validation of the algorithm was performed with six healthy volunteers, repeating the search twenty times for each subject. Results: The validation demonstrated that the closed-loop control worked as desired despite the large variation in the single-trial EEG responses. We were often able to get close to the orientation that maximizes the EEG amplitude with only a few tens of pulses. Conclusion: Optimizing stimulation with EEG feedback in a closed-loop manner is feasible and enables effective coupling to brain activity.
Original language | English |
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Pages (from-to) | 523-531 |
Number of pages | 9 |
Journal | Brain Stimulation |
Volume | 15 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Mar 2022 |
MoE publication type | A1 Journal article-refereed |
Keywords
- Bayesian optimization
- Closed-loop
- Electroencephalography
- Multi-channel TMS
- Multi-locus TMS
- Transcranial magnetic stimulation
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Dive into the research topics of 'Closed-loop optimization of transcranial magnetic stimulation with electroencephalography feedback'. Together they form a unique fingerprint.-
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ConnectToBrain: Connecting to the Networks of the Human Brain
Ilmoniemi, R. (Principal investigator), Aydogan, D. B. (Project Member), Sinisalo, H. (Project Member), Li, L. (Project Member), Mäkinen, A. (Project Member), Pankka, H. (Project Member), Souza, V. (Project Member), Makkonen, M. (Project Member), Nieminen, A. (Project Member), Nissilä, I. (Project Member), Laine, M. (Project Member), Parvin, S. (Project Member), Rissanen, I. (Project Member), Kicic, D. (Project Member), Lioumis, P. (Project Member), Koistinen, L. (Project Member), Kahilakoski, O.-P. (Project Member), Raij, T. (Project Member), Soto de la Cruz, A. (Project Member), Tommila, T. (Project Member), Ylöstalo, T. (Project Member), Ukharova, E. (Project Member), Metsomaa, J. (Project Member), Vaalto, S. (Project Member), Granö, I. (Project Member), Koponen, M. (Project Member), Roine, T. (Project Member), Ahola, O. (Project Member) & Hakulinen, K. (Project Member)
01/08/2019 → 31/08/2026
Project: EU: ERC grants
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Multi-locus transcranial magnetic stimulation
Nieminen, J. (Principal investigator), Tugin, S. (Project Member), Laine, M. (Project Member) & Nieminen, A. (Project Member)
01/09/2019 → 31/08/2021
Project: Academy of Finland: Other research funding
Equipment
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Aalto Neuroimaging Infrastructure
Jousmäki, V. (Manager)
School of ScienceFacility/equipment: Facility
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Press/Media
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News The Algorithm That Could Make Brain Stimulation More Reliable Medical practitioners have used transcranial magnetic brain stimulation (TMS) for years to treat brain disorders like chronic pain and depression. A new technique could improve TMS's effec
18/02/2022
1 item of Media coverage
Press/Media: Media appearance
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Using brain activity feedback to automate stimulation technique for treating disorders
15/02/2022
2 items of Media coverage
Press/Media: Media appearance