Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution

Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.

脑机接口（Brain-Computer Interfaces, BCIs）旨在将脑信号解码为控制指令，以帮助那些丧失肌肉控制能力的重度残障人士。这类无创脑机接口的潜在使用者，其身心状况存在显著差异。既往研究表明，脑机接口在患者群体中具备普适适用性，但存在两难困境：要么需要开展大量训练会话，要么仅能研究受限制程度中等的患者。本研究提出一款脑机接口系统，旨在极短时间内为重度运动障碍患者建立外部控制能力。仅通过6次实验会话，4名患者中有3名能够实现对该基于运动想象或意念执行的脑机接口的有效控制。针对病情最严重的患者，研究结果显示该脑机接口在控制精度、反应时与信息传输速率方面，均优于患者所使用的最优辅助技术（Assistive Technology, AT）。本次研究的成功，得益于采用了以用户为中心的设计思路，以及高度灵活的技术架构。当前最先进的机器学习方法，能够提取并融合脑电图（Electroencephalogram, EEG）中蕴含的多种相关特征，帮助患者快速实现有效的脑机接口控制。由此，本研究证实了灵活可定制的脑机接口应用，在重度残障用户群体中的可行性。该成果可被视为一项重大突破，原因有二：其一，研究结果在短时间内即可获得，契合严苛的临床需求；其二，与多数同类研究相比，本次参与研究的患者存在极为严重的交流障碍，他们日常依赖辅助技术维持生活，其中2名患者处于闭锁综合征状态。对于病情最严重的患者而言，现有辅助技术几乎无法实现可靠的交流。