Data_Sheet_1_A Hybrid Brain-Computer Interface Based on Visual Evoked Potential and Pupillary Response.docx

Brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP) has been widely studied due to the high information transfer rate (ITR), little user training, and wide subject applicability. However, there are also disadvantages such as visual discomfort and “BCI illiteracy.” To address these problems, this study proposes to use low-frequency stimulations (12 classes, 0.8–2.12 Hz with an interval of 0.12 Hz), which can simultaneously elicit visual evoked potential (VEP) and pupillary response (PR) to construct a hybrid BCI (h-BCI) system. Classification accuracy was calculated using supervised and unsupervised methods, respectively, and the hybrid accuracy was obtained using a decision fusion method to combine the information of VEP and PR. Online experimental results from 10 subjects showed that the averaged accuracy was 94.90 ± 2.34% (data length 1.5 s) for the supervised method and 91.88 ± 3.68% (data length 4 s) for the unsupervised method, which correspond to the ITR of 64.35 ± 3.07 bits/min (bpm) and 33.19 ± 2.38 bpm, respectively. Notably, the hybrid method achieved higher accuracy and ITR than that of VEP and PR for most subjects, especially for the short data length. Together with the subjects’ feedback on user experience, these results indicate that the proposed h-BCI with the low-frequency stimulation paradigm is more comfortable and favorable than the traditional SSVEP-BCI paradigm using the alpha frequency range.

基于稳态视觉诱发电位（Steady-State Visual Evoked Potential, SSVEP）的脑机接口（Brain-computer interface, BCI），因信息传输率（Information Transfer Rate, ITR）高、用户训练需求低、适用受试者群体广泛而得到了广泛研究。但该类系统也存在视觉不适与“BCI文盲”现象等缺陷。为解决上述问题，本研究提出采用低频刺激范式（包含12个刺激类别，频率范围为0.8~2.12 Hz，类间间隔为0.12 Hz），该刺激可同时诱发出视觉诱发电位（Visual Evoked Potential, VEP）与瞳孔反应（Pupillary Response, PR），进而构建混合脑机接口（hybrid BCI, h-BCI）系统。研究分别采用监督学习与无监督学习方法计算分类精度，并通过决策融合方法结合VEP与PR的特征信息，得到混合分类精度。来自10名受试者的在线实验结果表明：监督学习方法的平均分类精度为94.90±2.34%（数据时长1.5 s），无监督学习方法为91.88±3.68%（数据时长4 s），对应的信息传输率分别为64.35±3.07 比特/分钟（bits/min, bpm）与33.19±2.38 bpm。值得注意的是，相较于单独采用VEP或PR的分类方法，混合方法在大多数受试者中均实现了更高的分类精度与信息传输率，尤其在数据时长较短的场景下优势更为突出。结合受试者的用户体验反馈，上述结果表明：所提出的采用低频刺激范式的混合脑机接口，相较于传统的基于α频段的SSVEP-BCI范式，具有更优异的舒适度与用户接受度。