School of Fundamental Science and Technology, Graduate School of Science and Technology, Keio University, Kanagawa, Japan.
BMC Neurosci. 2010 Sep 16;11:117. doi: 10.1186/1471-2202-11-117.
For severely paralyzed people, a brain-computer interface (BCI) provides a way of re-establishing communication. Although subjects with muscular dystrophy (MD) appear to be potential BCI users, the actual long-term effects of BCI use on brain activities in MD subjects have yet to be clarified. To investigate these effects, we followed BCI use by a chronic tetraplegic subject with MD over 5 months. The topographic changes in an electroencephalogram (EEG) after long-term use of the virtual reality (VR)-based BCI were also assessed. Our originally developed BCI system was used to classify an EEG recorded over the sensorimotor cortex in real time and estimate the user's motor intention (MI) in 3 different limb movements: feet, left hand, and right hand. An avatar in the internet-based VR was controlled in accordance with the results of the EEG classification by the BCI. The subject was trained to control his avatar via the BCI by strolling in the VR for 1 hour a day and then continued the same training twice a month at his home.
After the training, the error rate of the EEG classification decreased from 40% to 28%. The subject successfully walked around in the VR using only his MI and chatted with other users through a voice-chat function embedded in the internet-based VR. With this improvement in BCI control, event-related desynchronization (ERD) following MI was significantly enhanced (p < 0.01) for feet MI (from -29% to -55%), left-hand MI (from -23% to -42%), and right-hand MI (from -22% to -51%).
These results show that our subject with severe MD was able to learn to control his EEG signal and communicate with other users through use of VR navigation and suggest that an internet-based VR has the potential to provide paralyzed people with the opportunity for easy communication.
对于严重瘫痪的人来说,脑机接口 (BCI) 提供了一种重新建立交流的方式。尽管肌萎缩性侧索硬化症 (MD) 患者似乎是潜在的 BCI 用户,但 BCI 长期使用对 MD 患者大脑活动的实际影响尚未得到明确。为了研究这些影响,我们对一位患有 MD 的慢性四肢瘫痪患者进行了 5 个月的 BCI 使用跟踪。还评估了长期使用基于虚拟现实 (VR) 的 BCI 后脑电图 (EEG) 的地形图变化。我们最初开发的 BCI 系统用于实时分类记录在感觉运动皮层上的 EEG,并估计用户在 3 种不同肢体运动中的运动意图 (MI):脚、左手和右手。根据 BCI 对 EEG 分类的结果,互联网上的 VR 中的一个化身被控制。该患者通过每天在 VR 中漫步 1 小时并在家中每月继续两次相同的训练来通过 BCI 控制他的化身。
经过训练,EEG 分类错误率从 40%降至 28%。该患者仅通过其 MI 在 VR 中四处走动,并通过互联网上 VR 中的语音聊天功能与其他用户聊天。随着 BCI 控制的这种改善,MI 后事件相关去同步 (ERD) 显著增强(p<0.01),用于脚部 MI(从-29%到-55%)、左手 MI(从-23%到-42%)和右手 MI(从-22%到-51%)。
这些结果表明,我们的 MD 严重患者能够学会控制他的 EEG 信号并通过使用 VR 导航与其他用户进行交流,并表明互联网 VR 有可能为瘫痪患者提供轻松交流的机会。