Shu Xiaokang, Chen Shugeng, Meng Jianjun, Yao Lin, Sheng Xinjun, Jia Jie, Farina Dario, Zhu Xiangyang
IEEE Trans Biomed Eng. 2018 Nov 19. doi: 10.1109/TBME.2018.2882075.
BCI decoding accuracy plays a crucial role in practical applications. With accurate feedback, BCI-based therapy determines beneficial neural plasticity in stroke patients. In this study, we aimed at improving sensorimotor rhythm (SMR)-based BCI performance by integrating motor tasks with tactile stimulation.
Eleven stroke patients were recruited for three experimental conditions, i.e., motor attempt (MA) condition, tactile stimulation (TS) condition, and tactile stimulation-assisted motor attempt (TS-MA) condition. Tactile stimulation was delivered to the paretic hand wrist during both task and idle states using a DC vibrator.
We observed that the TS-MA condition achieved greater motor-related cortical activation (MRCA) in alpha-beta band when compared with both TS and MA conditions. Consequently, online BCI decoding accuracies between task and idle states were significantly improved from 74.5% in the MA condition to 85.1% in the TS-MA condition (p < 0.001), whereas the accuracy in the TS condition was 54.6% (approaching to the chance level of 50%).
This finding demonstrates that sensory afferent from peripheral nerves benefits the neural process of sensorimotor cortex in stroke patients. With appropriate sensory stimulation, MRCA is enhanced and corresponding brain patterns are more discriminative.
This novel SMR-BCI paradigm shows great promise to facilitate the practical application of BCI-based stroke rehabilitation.
脑机接口(BCI)解码精度在实际应用中起着至关重要的作用。通过准确的反馈,基于BCI的治疗可确定中风患者有益的神经可塑性。在本研究中,我们旨在通过将运动任务与触觉刺激相结合来提高基于感觉运动节律(SMR)的BCI性能。
招募了11名中风患者,进行三种实验条件,即运动尝试(MA)条件、触觉刺激(TS)条件和触觉刺激辅助运动尝试(TS-MA)条件。在任务和空闲状态下,均使用直流振动器向患侧手腕提供触觉刺激。
我们观察到,与TS和MA条件相比,TS-MA条件在α-β频段实现了更大的运动相关皮层激活(MRCA)。因此,任务和空闲状态之间的在线BCI解码精度从MA条件下的74.5%显著提高到TS-MA条件下的85.1%(p < 0.001),而TS条件下的精度为54.6%(接近50%的机遇水平)。
这一发现表明,来自外周神经的感觉传入有利于中风患者感觉运动皮层的神经过程。通过适当的感觉刺激,MRCA增强,相应的脑电模式更具辨别力。
这种新型的SMR-BCI范式在促进基于BCI的中风康复的实际应用方面显示出巨大的潜力。