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基于增强现实刺激的稳态视觉诱发电位脑机接口控制类人机器人在迷宫中行走

Humanoid Robot Walking in Maze Controlled by SSVEP-BCI Based on Augmented Reality Stimulus.

作者信息

Zhang Shangen, Gao Xiaorong, Chen Xiaogang

机构信息

School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.

Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.

出版信息

Front Hum Neurosci. 2022 Jul 14;16:908050. doi: 10.3389/fnhum.2022.908050. eCollection 2022.

DOI:10.3389/fnhum.2022.908050
PMID:35911600
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9330178/
Abstract

The application study of robot control based brain-computer interface (BCI) not only helps to promote the practicality of BCI but also helps to promote the advancement of robot technology, which is of great significance. Among the many obstacles, the importability of the stimulator brings much inconvenience to the robot control task. In this study, augmented reality (AR) technology was employed as the visual stimulator of steady-state visual evoked potential (SSVEP)-BCI and the robot walking experiment in the maze was designed to testify the applicability of the AR-BCI system. The online experiment was designed to complete the robot maze walking task and the robot walking commands were sent out by BCI system, in which human intentions were decoded by Filter Bank Canonical Correlation Analysis (FBCCA) algorithm. The results showed that all the 12 subjects could complete the robot walking task in the maze, which verified the feasibility of the AR-SSVEP-NAO system. This study provided an application demonstration for the robot control base on brain-computer interface, and further provided a new method for the future portable BCI system.

摘要

基于脑机接口（BCI）的机器人控制应用研究不仅有助于提高BCI的实用性，还有助于推动机器人技术的进步，具有重要意义。在诸多障碍中，刺激器的可移植性给机器人控制任务带来诸多不便。本研究采用增强现实（AR）技术作为稳态视觉诱发电位（SSVEP）-BCI的视觉刺激器，并设计了迷宫中的机器人行走实验来验证AR-BCI系统的适用性。在线实验旨在完成机器人迷宫行走任务，机器人行走指令由BCI系统发出，其中人类意图通过滤波器组典型相关分析（FBCCA）算法进行解码。结果表明，12名受试者均能完成迷宫中的机器人行走任务，验证了AR-SSVEP-NAO系统的可行性。本研究为基于脑机接口的机器人控制提供了应用示范，并进一步为未来便携式BCI系统提供了新方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/a55868a14615/fnhum-16-908050-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/f6af3c059b0a/fnhum-16-908050-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/aac71a678fb1/fnhum-16-908050-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/4ba7456b4583/fnhum-16-908050-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/6123992ebd49/fnhum-16-908050-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/10fdb767a308/fnhum-16-908050-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/4ea2c2e649ed/fnhum-16-908050-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/a8caad66a2fa/fnhum-16-908050-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/a55868a14615/fnhum-16-908050-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/f6af3c059b0a/fnhum-16-908050-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/aac71a678fb1/fnhum-16-908050-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/4ba7456b4583/fnhum-16-908050-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/6123992ebd49/fnhum-16-908050-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/10fdb767a308/fnhum-16-908050-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/4ea2c2e649ed/fnhum-16-908050-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/a8caad66a2fa/fnhum-16-908050-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0176/9330178/a55868a14615/fnhum-16-908050-g008.jpg

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