Decoding the individualized excitability of motor cortex for closed-loop TMS-EEG with Deep learning

01.02.2025 bis 31.01.2026

Transcranial magnetic stimulation (TMS) has been widely recognized as a safe and non-invasive technique for neuromodulation in treating neurological disorders over the past several decades. However, the interplay between pre-stimulus brain states and the excitability induced by subsequent TMS pulses remains inadequately understood. To this end, the present study employs state-of-the-art deep learning techniques to decode the probability of excitability within the motor cortex using a closed-loop TMS-electroencephalography (EEG) paradigm. The ultimate objective of this research endeavor is to expedite the identification of personalized, precise, and intelligent non-invasive brain stimulation therapies in clinical.