IEEE Trans Neural Syst Rehabil Eng. 2022;30:1525-1535. doi: 10.1109/TNSRE.2022.3179971. Epub 2022 Jun 10.
The hybrid brain-computer interface (hBCI) combining motor imagery (MI) and steady-state visual evoked potential (SSVEP) has been proven to have better performance than a pure MI- or SSVEP-based brain-computer interface (BCI). In most studies on hBCIs, subjects have been required to focus their attention on flickering light-emitting diodes (LEDs) or blocks while imagining body movements. However, these two classical tasks performed concurrently have a poor correlation. Therefore, it is necessary to reduce the task complexity of such a system and improve its user-friendliness. Aiming to achieve this goal, this study proposes a novel hybrid BCI that combines MI and intermodulation SSVEPs. In the proposed system, images of both hands flicker at the same frequency (i.e., 30 Hz) but at different grasp frequencies (i.e., 1 Hz for the left hand, and 1.5 Hz for the right hand), resulting in different intermodulation frequencies for encoding targets. Additionally, movement observation for subjects can help to perform the MI task better. In this study, two types of brain signals are classified independently and then fused by a scoring mechanism based on the probability distribution of relevant parameters. The online verification results showed that the average accuracies of 12 healthy subjects and 11 stroke patients were 92.40 ± 7.45% and 73.07 ± 9.07%, respectively. The average accuracies of 10 healthy subjects in the MI, SSVEP, and hybrid tasks were 84.00 ± 12.81%, 80.75 ± 8.08%, and 89.00 ± 9.94%, respectively. The high recognition accuracy verifies the feasibility and robustness of the proposed system. This study provides a novel and natural paradigm for a hybrid BCI based on MI and SSVEP.
混合脑-机接口（hBCI）结合运动想象（MI）和稳态视觉诱发电位（SSVEP）已被证明比纯 MI 或 SSVEP 脑-机接口（BCI）具有更好的性能。在大多数 hBCI 研究中，要求受试者在想象身体运动的同时专注于闪烁的发光二极管（LED）或方块。然而，这两个经典任务同时执行的相关性较差。因此，有必要降低此类系统的任务复杂性并提高其用户友好性。为了实现这一目标，本研究提出了一种新的结合 MI 和中频调制 SSVEP 的混合脑机接口。在提出的系统中，双手的图像以相同的频率（即 30 Hz）闪烁，但以不同的抓握频率（即左手为 1 Hz，右手为 1.5 Hz），从而产生不同的中频调制频率用于编码目标。此外，为受试者提供运动观察有助于更好地执行 MI 任务。在这项研究中，两种类型的脑信号分别进行分类，然后通过基于相关参数概率分布的评分机制进行融合。在线验证结果表明，12 名健康受试者和 11 名中风患者的平均准确率分别为 92.40±7.45%和 73.07±9.07%。10 名健康受试者在 MI、SSVEP 和混合任务中的平均准确率分别为 84.00±12.81%、80.75±8.08%和 89.00±9.94%。高识别准确率验证了所提出系统的可行性和鲁棒性。本研究为基于 MI 和 SSVEP 的混合脑机接口提供了一种新颖且自然的范例。
