Evaluation of signal analysis algorithms for ipsilateral motor-evoked potentials induced by transcranial magnetic stimulation

• Published in: Journal of neural engineering Vol. 19; no. 3; pp. 36032 - 36038
• Main Authors: Guggenberger, Robert, Trunk, Bettina Hanna, Canbolat, Sine, Ziegler, Lukas, Gharabaghi, Alireza
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.06.2022
• Subjects: Adult; Algorithms; Electromyography; Evoked Potentials, Motor - physiology; Humans; ipsilateral; measurement; motor-evoked potential; Muscle, Skeletal - physiology; Reproducibility of Results; toolbox; transcranial magnetic stimulation; Transcranial Magnetic Stimulation - methods; toolbox; algorithms; ipsilateral; transcranial magnetic stimulation; motor-evoked potential; measurement
• ISSN: 1741-2560; 1741-2552; 1741-2552
• DOI: 10.1088/1741-2552/ac6dc4

Objective . Evaluating ipsilateral motor-evoked potentials (iMEP) induced by transcranial magnetic stimulation is challenging. In healthy adults, isometric contraction is necessary to facilitate iMEP induction; therefore, the signal may be masked by the concurrent muscle activity. Signal analysis algorithms for iMEP evaluation need to be benchmarked and evaluated. Approach . An open analysis toolbox for iMEP evaluation was implemented on the basis of 11 previously reported algorithms, which were all threshold based, and a new template-based method based on data-driven signal decomposition. The reliability and validity of these algorithms were evaluated with a dataset of 4244 iMEP from 55 healthy adults. Main results. iMEP estimation varies drastically between algorithms. Several algorithms exhibit high reliability, but some appear to be influenced by background activity of muscle preactivation. Especially in healthy subjects, template-based approaches might be more valid than threshold-based ones. Measurement of iMEP persistence requires algorithms that reject some trials as MEP negative. The stricter the algorithms reject trials, the less reliable they generally are. Our evaluation identifies an optimally strict and reliable algorithm. Significance. We show different benchmarks and propose application for different use cases.