發(fā)布時間：2018-10-08 21:54

【摘要】：腦—機(jī)接口（Brain-Computer Interface，BCI）是指在人腦和計算機(jī)或其他電子設(shè)備之間建立的直接的交流和控制通道。作為一種新穎的人機(jī)接口方式，腦—機(jī)接口使癱瘓的患者看到了與外界自主交互的新希望。無創(chuàng)式腦—機(jī)接口由于其成本低、對大腦無損傷等特點，是當(dāng)前腦—機(jī)接口研究的主要方向。有研究證實，腦—機(jī)接口對有運動障礙患者的恢復(fù)性訓(xùn)練有著潛在的臨床應(yīng)用背景，因此研發(fā)出具有實時性的腦—機(jī)接口在線系統(tǒng)具有十分重要的意義。 本課題來源于重慶市科技攻關(guān)計劃項目(CSTC,2009AC5023)。本文結(jié)合國內(nèi)外研究背景及現(xiàn)狀，以左右手運動想象腦電信號作為切入點，對大腦感覺運動皮層的事件相關(guān)去同步/同步現(xiàn)象（ERD/ERS）進(jìn)行了離線分析，尋求適合于腦—機(jī)接口在線系統(tǒng)的特征提取及模式識別算法，同時設(shè)計并實現(xiàn)了基于左右手運動想象的腦—機(jī)接口在線系統(tǒng)。 本文主要研究了基于左右手運動想象腦—機(jī)接口在線系統(tǒng)的實現(xiàn)方案，其中運動想象腦電特征信號的實時特征提取及分類識別是腦—機(jī)接口的關(guān)鍵技術(shù)部分。腦-機(jī)接口實驗中結(jié)合生理信號測量系統(tǒng)Active One和一臺PC機(jī)實現(xiàn)了基于左右手運動想象的腦—機(jī)接口在線系統(tǒng)。實驗采用無創(chuàng)性的頭皮電極采集運動想象腦電信號，，具有較好的應(yīng)用前景。 實驗過程中受試者注視視覺刺激器上播放的左右手運動圖像，左右手圖像代表不同肢體的運動想象刺激，通過Active One系統(tǒng)采集受試者運動想象的腦電信號，對信號進(jìn)行特征提取及分類識別，分析并判斷受試者想象的肢體。采用基于滑動窗的時域二階距估計法提取運動想象腦電特征并從能量的角度進(jìn)行分析，結(jié)合基于Fisher準(zhǔn)則函數(shù)的線性分類器對提取的特征進(jìn)行降維與識別。實驗結(jié)果表明，該腦—機(jī)接口系統(tǒng)能有效地提取出左右手運動想象特征并分類識別，能夠判斷出受試者所想象的肢體，將結(jié)果返回到人機(jī)交互界面。
[Abstract]:Brain-computer interface (Brain-Computer Interface,BCI) is a direct communication and control channel between human brain and computer or other electronic devices. As a novel man-machine interface, brain-computer interface (BCI) offers paralyzed patients a new hope for autonomous interaction with the outside world. Non-invasive brain-computer interface (BCI) is the main research direction of brain-computer interface (BCI) because of its low cost and no damage to the brain. Some studies have confirmed that BCI has a potential clinical application background for patients with motor disorders, so it is of great significance to develop a real time BCI online system. This topic comes from Chongqing Science and Technology key Project (CSTC,2009AC5023). In this paper, based on the research background and present situation at home and abroad, the event related desynchronization / synchronization (ERD/ERS) in the sensorimotor cortex is analyzed offline by using the left and right hand motion imagination EEG as the starting point. The feature extraction and pattern recognition algorithms suitable for the online brain-computer interface system are sought. At the same time, an on-line brain-computer interface system based on left-right motion imagination is designed and implemented. In this paper, we mainly study the realization scheme of the online system based on the left and right hand motion imagining brain-computer interface, in which the real-time feature extraction and classification recognition of the motion imagination EEG feature signal is the key technology part of the brain-computer interface. In the brain-computer interface experiment, an online brain-computer interface system based on left-right hand motion imagination is realized by combining the physiological signal measurement system (Active One) and a PC machine. The experiment uses non-invasive scalp electrode to collect motion imagination EEG signal, which has a good application prospect. In the course of the experiment, the subjects gazed at the left and right hand motion images played on the visual stimulator, and the left and right hand images represented the motion imagination stimuli of different limbs. The EEG signals of the subjects' motion imagination were collected through the Active One system. The feature extraction and classification of the signals were carried out, and the imaginary limbs of the subjects were analyzed and judged. The second order distance estimation method based on sliding window is used to extract the motion imaginary EEG features and analyze them from the point of view of energy. The extracted features are reduced and recognized by a linear classifier based on Fisher criterion function. The experimental results show that the brain-computer interface system can effectively extract the left and right hand motion imagination features and classify and recognize, can judge the subjects' imaginary limbs, and return the results to the human-computer interface.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TP334.7

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 魏燕,馬偉順;LABVIEW在《信號處理》教學(xué)中的應(yīng)用[J];長春師范學(xué)院學(xué)報;2005年05期

2 葉檸;孫宇舸;王旭;;基于共空間模式和K近鄰分類器的腦-機(jī)接口信號分類方法[J];東北大學(xué)學(xué)報(自然科學(xué)版);2009年08期

3 楊立才,李佰敏,李光林,賈磊;腦-機(jī)接口技術(shù)綜述[J];電子學(xué)報;2005年07期

4 朱志強(qiáng),田心;LabVIEW及其在生物醫(yī)學(xué)工程中的應(yīng)用[J];國外醫(yī)學(xué).生物醫(yī)學(xué)工程分冊;2001年02期

5 程光輝;石銳;何慶華;;Windows環(huán)境下腦機(jī)接口視覺刺激器的設(shè)計[J];計算機(jī)工程與應(yīng)用;2006年13期

6 胥彪;石銳;何慶華;;腦機(jī)接口(BCI)系統(tǒng)的實時數(shù)據(jù)傳輸技術(shù)研究[J];計算機(jī)科學(xué);2007年09期

7 肖首柏;胡劍鋒;;腦機(jī)接口研究概述[J];科技廣場;2007年09期

8 許慰玲,黃靜霞,沈民奮;基于小波包分解的時變腦電節(jié)律提取[J];數(shù)據(jù)采集與處理;2004年01期

9 何慶華,彭承琳,吳寶明,王禾;基于視覺誘發(fā)電位的腦機(jī)接口實驗研究[J];生物醫(yī)學(xué)工程學(xué)雜志;2004年01期

10 劉琳;魏慶國;;CSSD+AAR模型在腦電信號處理中的應(yīng)用[J];通信技術(shù);2009年10期

相關(guān)博士學(xué)位論文 前2條

1 何慶華;基于視覺誘發(fā)電位的腦機(jī)接口實驗研究[D];重慶大學(xué);2003年

2 郭曉靜;獨立分量分析在腦—機(jī)接口中的應(yīng)用研究[D];安徽大學(xué);2010年

相關(guān)碩士學(xué)位論文 前4條

1 梁爽;基于3D視覺刺激器的BCI系統(tǒng)的設(shè)計與實現(xiàn)[D];重慶大學(xué);2011年

2 張爽;基于左右手運動想象的腦—機(jī)接口的算法研究[D];天津大學(xué);2007年

3 范樹凱;非線性調(diào)頻信號的時頻分析及其應(yīng)用[D];江南大學(xué);2008年

4 周雅;基于左右手運動想象腦—機(jī)接口的系統(tǒng)研究[D];重慶大學(xué);2010年

本文編號：2258402