Enhancing visual brain-computer interface through V1-targeted RTMS by modulating visual attention

Brain-computer interfaces (BCIs) enable users to control devices directly through brain activity. Despite recent advancements in machine-learning algorithms, the signal-to-noise ratio (SNR) of the brain's responses still limits decoding performance, highlighting the necessity for targeted neuromodulation techniques to overcome this limitation. To evaluate whether 5 Hz repetitive transcranial magnetic stimulation (rTMS) targeting the primary visual cortex (V1) can enhance SSVEP-based BCI performance by improving neural signal SNR and modulating visual network dynamics. Twenty-four healthy subjects underwent both real and sham rTMS in a randomized order. The rTMS was precisely implemented through magnetic resonance imaging (MRI)-guided navigation to stimulate V1 in participants. Electroencephalograms (EEGs) were recorded during SSVEP tasks and resting-state before, immediately after, and 20 min after rTMS. SSVEP tasks were conducted across four frequency bands: low frequency (LF: 8-12 Hz), middle frequency (MF: 18-22 Hz), high frequency (HF: 28-32 Hz), and super high frequency (SHF: 38-42 Hz). The discriminability of BCI commands in the MF (+7.53%) and HF (+11.4%) bands significantly improved ( $p<0.001$ ), driven by enhanced prominence of both fundamental and harmonic components ( $p<0.01$ ). Quantitative analysis indicated that the improved SNR was due to the suppression of the background activity ( $p<0.05$ ). This effect was linked to rTMS-induced enhancements in visual attention, evidenced by increased occurrence and contribution of microstate B during the SSVEP task ( $p<0.01$ ). This study highlights the potential of 5 Hz rTMS as an effective neuromodulatory tool for optimizing BCI performance, particularly through facilitating visual attention.