Investigating Visual Imagery as a BCI Control Strategy: A Pilot Study

Brain-Computer Interface (BCI) technology may provide individuals with motor impairments or even the general population a new way to interact with the world around them. However, current BCI systems using electroencephalography (EEG) can be unreliable and produce large variations in performance. Most studies seek to improve performance by focusing on signal processing and classification techniques. However, it may also be beneficial to investigate different control strategies. For this reason, the main objective of this pilot study was to investigate the use of visual imagery, a control paradigm that has not been much tested for EEG BCI applications. Visual imagery may provide a more intuitive control strategy with a greater number of available classes than other popular imagery-based methods such as motor imagery. Using this paradigm, we have demonstrated above chance binary classification accuracy (59.9%, p < 0.05) during offline decoding of face and scene visual imagery. Furthermore, the participant in this study achieved significantly above chance performance during a three-class, closed-loop BCI interaction (47.2%, p = 0.05). The initial results of this pilot study demonstrate the feasibility of using visual imagery as an alternative EEG BCI control paradigm.

脑部计算机界面（BCI）技术可能会为个人提供运动障碍，甚至为普通人群提供与周围世界互动的新方法。但是，当前使用脑电图（EEG）的BCI系统可能是不可靠的，并且性能会产生巨大的差异。大多数研究试图通过专注于信号处理和分类技术来提高性能。但是，研究不同的控制策略也可能是有益的。因此，这项试验研究的主要目的是研究视觉图像的使用，视觉图像的使用是对脑电图BCI应用没有太多测试的控制范式。与其他基于图像的方法（如运动图像）相比，视觉图像可以提供更直观的控制策略，并提供更多可用类的可用类。使用此范式，我们在离线式和场景视觉图像的离线解码过程中证明了上述机会二进制分类精度（59.9％，p <0.05）。此外，这项研究的参与者在三类闭环BCI相互作用中的机会表现显着高于机会性能（47.2％，p = 0.05）。这项试验研究的最初结果表明，将视觉图像用作替代性EEG BCI控制范式的可行性。