北京生物医学工程

基于Matlab环境的脑控轮椅搭建与实验验证

Construction and validation of SSVEP brain- controlled wheelchair system based on Matlab environment

作者：刘明王康宁陈小刚王瑶王慧泉蒲江波谢小波王金海徐圣普

单位：中国医学科学院北京协和医学院生物医学工程研究所(天津 300192) 天津工业大学电子与信息工程学院(天津 300387)

关键词：脑控轮椅；稳态视觉诱发电位；脑-机接口；脑电；性能评价

分类号：R318.04; R318.6

出版年·卷·期（页码）：2019·38·2（190-197）

摘要：

目的基于脑电(electroencephalography，EEG)的脑控轮椅（brain-controlled wheelchair，BCW）能够为无法通过四肢操控轮椅运动的严重肢体残疾或运动障碍患者提供辅助，满足日常移动或出行需要。本文以兼顾系统性价比和准确率为研究目的，采用便携脑电放大器，拟搭建一个基于Matlab环境的BCW系统，并验证系统的可行性和实用性。方法首先搭建一个基于稳态视觉诱发电位(steady-state visual evoked potential，SSVEP)的BCW系统，系统主要包括脑电刺激、采集与处理以及轮椅控制两大部分，用户无需长期训练即可通过脑电控制轮椅的运动状态。然后招募3名健康受试者进行系统分类准确率验证实验和预设路径控制验证实验。其中，分类准确率验证实验要求受试者按照语音提示指令，注视对应刺激闪烁块以得到分类结果；预设路径控制验证实验要求受试者完成三个轮椅既定路线控制任务。实验后填写问卷调查衡量本系统的控制难度、受试者舒适度和疲劳程度。结果比较提示指令与分类结果得到系统分类准确率为97%±1%。路径控制实验中受试者均能控制轮椅按照预设路径运动到目的地，且获得用时、实际路径长度、命令个数、时间优化率、路径优化率等指标。结论本文搭建的基于Matlab环境的SSVEP-BCW系统分类准确率较高，控制效果和控制舒适度较好，具有一定的实用性。

Objective Brain-controlled wheelchair (BCW) based on electroencephalography (EEG) can provide special assistance for severely disabled individual or movement disorders. Therefore, this paper intends to build a BCW system based on Matlab environment for high accuracy, portability and low cost. And we verify the feasibility and practicability of the system through experiments. Methods First, we construct a BCW system based on steady-state visual evoked potential (SSVEP). The system mainly includes two parts: EEG stimulation, acquisition and processing, and wheelchair control. Users can control the movement of the wheelchair through EEG without long-term training. Then three healthy subjects are recruited for the verification experiments of system classification accuracy and the preset path control. The verification experiment of the classification accuracy requires the subjects to gaze at the corresponding stimulus flicker block according to the voice guide to obtain the classification results. In verification experiment of preset path control, subjects drive the BCW following 3 predefined paths. Finally, questionnaires are filled in to measure the operation difficulty of the system, fatigue and comfort level of the subjects. Results By comparing the classification result with the prompt instruction, we can get that the classification accuracy of the system is 97%±1%. All subjects can control the wheelchair to move to the destination according to the preset path in the path control experiment, and total time, actual path, total number of commands, time optimality ratio, path length optimality ratio are obtained. Conclusions The classification accuracy of SSVEP-BCW system built in this paper based on Matlab environment is high, control effect is good, with certain practicability.

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