北京生物醫(yī)學(xué)工程

基于Matlab環(huán)境的腦控輪椅搭建與實(shí)驗(yàn)驗(yàn)證

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

作者：劉明王康寧陳小剛王瑤王慧泉蒲江波謝小波王金海徐圣普

單位：中國(guó)醫(yī)學(xué)科學(xué)院北京協(xié)和醫(yī)學(xué)院生物醫(yī)學(xué)工程研究所(天津 300192) 天津工業(yè)大學(xué)電子與信息工程學(xué)院(天津 300387)

關(guān)鍵詞：腦控輪椅；穩(wěn)態(tài)視覺(jué)誘發(fā)電位；腦-機(jī)接口；腦電；性能評(píng)價(jià)

分類號(hào)：R318.04; R318.6

出版年·卷·期（頁(yè)碼）：2019·38·2（190-197）

摘要：

目的基于腦電(electroencephalography，EEG)的腦控輪椅（brain-controlled wheelchair，BCW）能夠?yàn)闊o(wú)法通過(guò)四肢操控輪椅運(yùn)動(dòng)的嚴(yán)重肢體殘疾或運(yùn)動(dòng)障礙患者提供輔助，滿足日常移動(dòng)或出行需要。本文以兼顧系統(tǒng)性價(jià)比和準(zhǔn)確率為研究目的，采用便攜腦電放大器，擬搭建一個(gè)基于Matlab環(huán)境的BCW系統(tǒng)，并驗(yàn)證系統(tǒng)的可行性和實(shí)用性。方法首先搭建一個(gè)基于穩(wěn)態(tài)視覺(jué)誘發(fā)電位(steady-state visual evoked potential，SSVEP)的BCW系統(tǒng)，系統(tǒng)主要包括腦電刺激、采集與處理以及輪椅控制兩大部分，用戶無(wú)需長(zhǎng)期訓(xùn)練即可通過(guò)腦電控制輪椅的運(yùn)動(dòng)狀態(tài)。然后招募3名健康受試者進(jìn)行系統(tǒng)分類準(zhǔn)確率驗(yàn)證實(shí)驗(yàn)和預(yù)設(shè)路徑控制驗(yàn)證實(shí)驗(yàn)。其中，分類準(zhǔn)確率驗(yàn)證實(shí)驗(yàn)要求受試者按照語(yǔ)音提示指令，注視對(duì)應(yīng)刺激閃爍塊以得到分類結(jié)果；預(yù)設(shè)路徑控制驗(yàn)證實(shí)驗(yàn)要求受試者完成三個(gè)輪椅既定路線控制任務(wù)。實(shí)驗(yàn)后填寫問(wèn)卷調(diào)查衡量本系統(tǒng)的控制難度、受試者舒適度和疲勞程度。結(jié)果比較提示指令與分類結(jié)果得到系統(tǒng)分類準(zhǔn)確率為97%±1%。路徑控制實(shí)驗(yàn)中受試者均能控制輪椅按照預(yù)設(shè)路徑運(yùn)動(dòng)到目的地，且獲得用時(shí)、實(shí)際路徑長(zhǎng)度、命令個(gè)數(shù)、時(shí)間優(yōu)化率、路徑優(yōu)化率等指標(biāo)。結(jié)論本文搭建的基于Matlab環(huán)境的SSVEP-BCW系統(tǒng)分類準(zhǔn)確率較高，控制效果和控制舒適度較好，具有一定的實(shí)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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