Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin 300384, China.
Endowed Research Department of Clinical Neuroengineering, Global Center for Medical Engineering and Informatics, Osaka University, Osaka 565-0871, Japan.
Biomed Res Int. 2017;2017:8316485. doi: 10.1155/2017/8316485. Epub 2017 Jun 1.
A novel hybrid brain-computer interface (BCI) based on the electroencephalogram (EEG) signal which consists of a motor imagery- (MI-) based online interactive brain-controlled switch, "teeth clenching" state detector, and a steady-state visual evoked potential- (SSVEP-) based BCI was proposed to provide multidimensional BCI control. MI-based BCI was used as single-pole double throw brain switch (SPDTBS). By combining the SPDTBS with 4-class SSEVP-based BCI, movement of robotic arm was controlled in three-dimensional (3D) space. In addition, muscle artifact (EMG) of "teeth clenching" condition recorded from EEG signal was detected and employed as interrupter, which can initialize the statement of SPDTBS. Real-time writing task was implemented to verify the reliability of the proposed noninvasive hybrid EEG-EMG-BCI. Eight subjects participated in this study and succeeded to manipulate a robotic arm in 3D space to write some English letters. The mean decoding accuracy of writing task was 0.93 ± 0.03. Four subjects achieved the optimal criteria of writing the word "HI" which is the minimum movement of robotic arm directions (15 steps). Other subjects had needed to take from 2 to 4 additional steps to finish the whole process. These results suggested that our proposed hybrid noninvasive EEG-EMG-BCI was robust and efficient for real-time multidimensional robotic arm control.
一种基于脑电图（EEG）信号的新型混合脑机接口（BCI），由基于运动想象（MI）的在线交互脑控制开关、“咬牙”状态检测器和基于稳态视觉诱发电位（SSVEP）的 BCI 组成，旨在提供多维 BCI 控制。基于 MI 的 BCI 用作单刀双掷脑开关（SPDTBS）。通过将 SPDTBS 与基于 4 类 SSEVP 的 BCI 相结合，可以控制机器人手臂在三维（3D）空间中的运动。此外，从 EEG 信号中记录的“咬牙”状态的肌电（EMG）被检测并用作中断器，可初始化 SPDTBS 的语句。实施实时书写任务以验证所提出的非侵入性混合 EEG-EMG-BCI 的可靠性。八名受试者参与了这项研究，并成功地在 3D 空间中操纵机器人手臂来书写一些英文字母。书写任务的平均解码准确率为 0.93±0.03。四名受试者达到了书写单词“HI”的最佳标准，这是机器人手臂方向的最小运动（15 步）。其他受试者需要再走 2 到 4 步才能完成整个过程。这些结果表明，我们提出的混合非侵入性 EEG-EMG-BCI 对于实时多维机器人手臂控制是强大且高效的。
