本文選題：腦-機接口　切入點：序貫概率比檢驗　出處：《大連理工大學》2013年碩士論文　論文類型：學位論文

【摘要】：腑－機接口(Brain-computer Interface, BCI)是不依賴神經(jīng)肌肉組織的信息交流通道,它為患有運動障礙的病人與外界交互提供了一種新的手段。BCI的出現(xiàn)最初是以臨床應(yīng)用為目的,但近年來也已開始向非臨床應(yīng)用領(lǐng)域發(fā)展,所以BCI系統(tǒng)的在線實現(xiàn)具有十分重要的研究意義和應(yīng)用價值。 BCI系統(tǒng)的建立包括離線分析和在線實現(xiàn)兩部分。目前雖然有許多關(guān)于腦電信號分析方法的介紹,并在競賽數(shù)據(jù)中取得很高的分類正確率,但有許多方法因運算速度、算法復雜度等原因并不具有實用性,因此算法的實時性是確保在線實現(xiàn)的關(guān)鍵。本文著眼于建立基于左右于運動想象的BCI系統(tǒng),并在提供給受試者實時反饋的情況下實現(xiàn)光標的方向控制,基于此本文分別使用自回歸(Autoregression, AR)模型和自適應(yīng)Morlet小波基作為特征提取方法,結(jié)合根據(jù)判決理論建立序貫假設(shè)檢驗(Sequence Probability Ratio Test, SPRT)和序貫判別分析(Sequential Linear Discriminant Analysis, SLDA)兩種分類器,對國際BCI競賽提供的數(shù)據(jù)集進行離線分析,驗證算法識別準確率,實現(xiàn)動態(tài)分類。競賽數(shù)據(jù)的仿真分析結(jié)果表明：采用自適應(yīng)Morlet小波基提取特征比AR模型法能夠取得更高的分類準確率,在同樣的特征提取方法下,兩種分類器對競賽數(shù)據(jù)的分類效果相當。 為驗證算法的實時性能及實用性,本文基于BCI2000軟件平臺,以事件相關(guān)去同步、同步為基礎(chǔ),設(shè)計了左右手運動想象實驗,該實驗包含校準實驗以及光標控制實驗兩個部分。離線分析中,首先使用BCI2000的離線分析工具對每位受試者定位兩種任務(wù)類型下最具區(qū)分度的導聯(lián)位置,然后采用自適應(yīng)小波基進行特征提取,在4位受試者初次的實驗數(shù)據(jù)中,SPRT獲得了72.88%的平均正確率,SLDA獲得76.21%的平均準確率,且SLDA所需判決時間要短于SPRT�；陔x線分析結(jié)果,選取兩名準確率最高的受試參與光標控制實驗,采用自適應(yīng)小波基結(jié)合SLDA作為實時算法,經(jīng)訓練后兩名受試的平準正確率均高于80%。該研究為國內(nèi)在線運動想象BCI系統(tǒng)的一個重要突破,也為今后的BCI系統(tǒng)的實時性及實用化研究積累了經(jīng)驗。
[Abstract]:Brain-computer Interface (BCI) is a channel of information exchange independent of neuromuscular tissue, which provides a new means for patients with motor disorders to interact with the outside world. BCI was originally used for clinical purposes. However, in recent years, the field of non-clinical application has begun to develop, so the online implementation of BCI system has very important research significance and application value. The establishment of BCI system consists of offline analysis and online implementation. Although there are many introductions on EEG analysis methods and high classification accuracy in race data, there are many methods due to the speed of operation. The complexity of the algorithm is not practical, so the real-time of the algorithm is the key to ensure the online implementation. This paper focuses on the establishment of a BCI system based on the left and right motion imagination. In this paper, autoregressive autoregressive (ARG) model and adaptive Morlet wavelet basis are used as feature extraction methods. Two classifiers, Sequence Probability Ratio Test (SPRT) and Sequential Linear Discriminant Analysis (SLDAs), are established according to the decision theory. The data set provided by the international BCI competition is analyzed offline, and the recognition accuracy of the algorithm is verified. The simulation results of competition data show that the adaptive Morlet wavelet basis method can achieve higher classification accuracy than AR model. The two classifiers have the same classification effect on contest data. In order to verify the real-time performance and practicability of the algorithm, based on the BCI2000 software platform, based on the event related de-synchronization and synchronization, this paper designs the left-right hand motion imagination experiment. The experiment consists of two parts: calibration experiment and cursor control experiment. In offline analysis, we first use BCI2000's off-line analysis tool to locate the most differentiated lead position for each of the two task types. Then adaptive wavelet basis is used to extract the features. In the first experiment data of 4 subjects, the average correct rate of 72.88% and the average accuracy of 76.21% are obtained, and the decision time of SLDA is shorter than that of SPRT.Based on the results of off-line analysis, Two subjects with the highest accuracy were selected to participate in the cursor control experiment, and the adaptive wavelet base combined with SLDA was used as the real-time algorithm. After training, the accuracy rate of the two subjects is higher than that of 80%. This research is an important breakthrough in domestic online motion imagination BCI system, and it also accumulates experience for the real-time and practical research of BCI system in the future.
【學位授予單位】：大連理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：R318

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