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 ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi> <mml:mo><</mml:mo> <mml:mn>0.001</mml:mn></mml:mrow> </mml:math> ), driven by enhanced prominence of both fundamental and harmonic components ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi> <mml:mo><</mml:mo> <mml:mn>0.01</mml:mn></mml:mrow> </mml:math> ). Quantitative analysis indicated that the improved SNR was due to the suppression of the background activity ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi> <mml:mo><</mml:mo> <mml:mn>0.05</mml:mn></mml:mrow> </mml:math> ). This effect was linked to rTMS-induced enhancements in visual attention, evidenced by increased occurrence and contribution of microstate B during the SSVEP task ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>p</mml:mi> <mml:mo><</mml:mo> <mml:mn>0.01</mml:mn></mml:mrow> </mml:math> ). This study highlights the potential of 5 Hz rTMS as an effective neuromodulatory tool for optimizing BCI performance, particularly through facilitating visual attention.
Authors
- Ying Gao (ORCID: https://orcid.org/0009-0007-3741-6383)
- Xinyi Zhang (ORCID: https://orcid.org/0000-0002-6739-3676)
- Huiguang He (ORCID: https://orcid.org/0000-0002-0684-1711)
- Yijun Wang (ORCID: https://orcid.org/0000-0002-8161-2150)
- Shuang Qiu (ORCID: https://orcid.org/0000-0002-8327-2567)
- S.R Wang
Institutions
- Chinese Academy of Sciences (CN)
- Institute of Computing Technology (CN)
- State Key Laboratory on Integrated Optoelectronics (CN)
- Center for Excellence in Brain Science and Intelligence Technology (CN)
- Institute of Semiconductors (CN)
- Shanghai Center for Brain Science and Brain-Inspired Technology (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Imaging Neuroscience
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1162/imag.a.1013
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China