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<p>Steady state visually evoked potentials (SSVEPs) are a popular type of control signals in brain-computer interfaces (BCIs), in which they are typically elicited by observing a visual stimulus flashing at a specific frequency. For some patients, using SSVEP as control signal for a BCI can be difficult, for instance if they are unable to focus their gaze over the visual stimuli. To address this issue, some approaches were presented to design a gaze-independent SSVEP-controlled BCI but some difficulties have been reported, for instance for patients suffering from locked-in syndrome. In this work we employ a visual imagery (VI) signal, in which the visual stimulus is imagined instead of observed, to drive a BCI system and offer an alternative for patients that encounter issues with standard SSVEP approaches. We tested the proposed approach with 20 untrained subjects within a 3-classes BCI resulting in an offline classification accuracy of 60.93%. These results demonstrate how this gaze-independent BCI can be used by inexperienced BCI users.</p>

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A visual imagery paradigm for BCI strategies using imagined flickering patterns

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月4日

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  • Simone Priori,
  • Paolo Ricci,
  • Davide Consoli,
  • Arturo Micheli,
  • Adrien Merlini,
  • Francesco P. Andriulli

摘要

Steady state visually evoked potentials (SSVEPs) are a popular type of control signals in brain-computer interfaces (BCIs), in which they are typically elicited by observing a visual stimulus flashing at a specific frequency. For some patients, using SSVEP as control signal for a BCI can be difficult, for instance if they are unable to focus their gaze over the visual stimuli. To address this issue, some approaches were presented to design a gaze-independent SSVEP-controlled BCI but some difficulties have been reported, for instance for patients suffering from locked-in syndrome. In this work we employ a visual imagery (VI) signal, in which the visual stimulus is imagined instead of observed, to drive a BCI system and offer an alternative for patients that encounter issues with standard SSVEP approaches. We tested the proposed approach with 20 untrained subjects within a 3-classes BCI resulting in an offline classification accuracy of 60.93%. These results demonstrate how this gaze-independent BCI can be used by inexperienced BCI users.