Park, Tae Young Franke, Loraine Pieper, Steve Haehn, Daniel Ning, Lipeng 2024-04-24T07:34:26Z 2024-04-24T07:34:26Z 2024-04-11 2024-05 2093-9868 https://pubs.kist.re.kr/handle/201004/149691 Transcranial magnetic stimulation (TMS) is a device-based neuromodulation technique increasingly used to treat brain diseases. Electric field (E-field) modeling is an important technique in several TMS clinical applications, including the precision stimulation of brain targets with accurate stimulation density for the treatment of mental disorders and the localization of brain function areas for neurosurgical planning. Classical methods for E-field modeling usually take a long computation time. Fast algorithms are usually developed with significantly lower spatial resolutions that reduce the prediction accuracy and limit their usage in real-time or near real-time TMS applications. This review paper discusses several modern algorithms for real-time or near real-time TMS E-field modeling and their advantages and limitations. The reviewed methods include techniques such as basis representation techniques and deep neural-network-based methods. This paper also provides a review of software tools that can integrate E-field modeling with navigated TMS, including a recent software for real-time navigated E-field mapping based on deep neural-network models. English 대한의용생체공학회 A review of algorithms and software for real-time electric field modeling techniques for transcranial magnetic stimulation Article 10.1007/s13534-024-00373-4 1 Biomedical Engineering Letters (BMEL), v.14, no.3, pp.393 - 405 Biomedical Engineering Letters (BMEL) 14 3 393 405 N scie scopus kci 001194638600001 2-s2.0-85188884527 Engineering, Biomedical Engineering Review DOUBLE-BLIND TMS DEPRESSION APPROXIMATION CONNECTIVITY COMPUTATION EFFICACY TARGETS Transcranial magnetic stimulation Electric field modeling Deep neural networks Real-time prediction Navigated TMS