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The efficacy of transcranial magnetic stimulation (TMS) is influenced by the brain’s real-time activity state. This study aimed to investigate the correlation between cortical excitability states and EEG features, specifically the phase and power of the sensorimotor μ rhythm. We developed a high-precision real-time phase prediction algorithm based on a Long Short-Term Memory (LSTM) network and constructed a closed-loop TMS system dependent on EEG phase and power. Thirty healthy subjects were recruited for single-pulse TMS experiments. Motor evoked potentials (MEPs) and TMS-evoked potentials (TEPs) were recorded simultaneously to assess cortical excitability states triggered in real time based on different EEG phase and power features. The results demonstrated no significant correlation between the μ rhythm phase and the amplitudes of MEPs or most TEP components. In contrast, pre-stimulus μ rhythm power showed a significant positive correlation with MEP amplitude. Under high-power conditions, the amplitude of the late P180 component in the sensorimotor cortex was significantly enhanced. The early-to-mid components (N15-N100) of the global mean field potential (GMFP) also exhibited significantly increased amplitudes. This study found that, compared to phase, EEG μ rhythm power exhibits a more significant correlation with TMS-assessed cortical excitability states. This finding provides a key basis for developing EEG power-dependent closed-loop TMS methods to enhance the efficacy of TMS modulation.
Zhao et al. (Tue,) studied this question.