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ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Neural Technology
Volume 19 - 2025 | doi: 10.3389/fnins.2025.1506104
Improvement of BCI Performance with Bimodal SSMVEPs: Enhancing Response Intensity and Reducing Fatigue
Provisionally accepted- 1 Xinjiang University, Urumqi, Xinjiang Uyghur Region, China
- 2 Xi'an Jiaotong University, Xi'an, China
Steady-state visual evoked potential (SSVEP) is a widely used brain-computer interface (BCI) paradigm, valued for its multi-target capability and limited EEG electrode requirements.Conventional SSVEP methods frequently lead to visual fatigue and decreased recognition accuracy because of the flickering light stimulation. To address these issues, we developed an innovative steady-state motion visual evoked potential (SSMVEP) paradigm that integrated motion and color stimuli, designed specifically for augmented reality (AR) glasses. Our study aimed to enhance SSMVEP response intensity and reduce visual fatigue. Experiments were conducted under controlled laboratory conditions. EEG data were analyzed using the deep learning algorithm of EEGNet and fast Fourier transform (FFT) to calculate the classification accuracy and assess the response intensity.Experimental results showed that the bimodal motion-color integrated paradigm significantly outperformed single-motion SSMVEP and single-color SSVEP paradigms, respectively, achieving the highest accuracy of 83.81 % ± 6.52 % under the medium brightness (M) and area ratio of C of 0.6.Enhanced signal-to-noise ratio (SNR) and reduced visual fatigue were also observed, as confirmed by objective measures and subjective reports. The findings verified the bimodal paradigm as a novel application in SSVEP-based BCIs, enhancing both brain response intensity and user comfort.
Keywords: brain-computer interface (BCI), Steady-state motion visual evoked potential (SSMVEP), Bimodal Motion-Color Stimuli, Augmented reality (AR) glasses, signal-to-noise ratio
Received: 04 Oct 2024; Accepted: 17 Feb 2025.
Copyright: © 2025 刘, Xie, Zhang, Yang, Shao and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Jun Xie, Xi'an Jiaotong University, Xi'an, China
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