發(fā)布時間：2018-04-12 18:18

  本文選題：腦機接口 + 半監(jiān)督學習　； 參考：《華南理工大學》2015年碩士論文

【摘要】：腦機接口(BCI)可以不依賴于人的外部肌肉以及神經(jīng)組織,直接通過腦與外部環(huán)境建立通信機制,因此主要應用于有運動障礙的殘疾人以及神經(jīng)障礙病人的功能輔助與康復。經(jīng)過數(shù)十年的發(fā)展,BCI的系統(tǒng)框架和基本方法已經(jīng)較為完善,但在應用領(lǐng)域仍有大量亟待解決的實際問題。本文針對BCI系統(tǒng)訓練時間長的問題以及在線BCI系統(tǒng)的高分類準確率與實時性的特定要求,從兩個方面展開了研究工作:1)基于LDA算法提出了一種半監(jiān)督線性判別分析算法—SUST-ILDA。為了減少系統(tǒng)使用之前的訓練時間,采用半監(jiān)督學習的方式。首先,利用少量有標記樣本訓練初始LDA分類器模型。之后,采用自訓練的方法,利用在線獲得的無標記樣本逐步對分類器進行更新,以改善分類器的性能;為了減少在線計算復雜度,推導了基于LDA的增量更新模型。理論上,與現(xiàn)有在線半監(jiān)督學習算法SUST-LSSVM相比,計算復雜度大幅降低且穩(wěn)定。采用第三次腦機接口競賽數(shù)據(jù)進行實驗分析,證實了隨著在線樣本數(shù)增加,所提出的算法可以取得與SUST-LSSVM相似的分類準確率,并且穩(wěn)定收斂。2)設計并實現(xiàn)了一個基于P300的在線半監(jiān)督字符輸入腦機接口系統(tǒng)。與傳統(tǒng)有監(jiān)督P300字符輸入腦機接口系統(tǒng)相比,本系統(tǒng)的特點是:1)只需要較短時間的有監(jiān)督訓練之后,即可自動切換到輸入模式,大大減少了用戶使用系統(tǒng)之前單調(diào)沉悶的訓練過程。并且隨著字符的輸入,系統(tǒng)的分類準確率不斷提高,達到穩(wěn)定;2)在系統(tǒng)正常使用階段,利用在線無標簽樣本對分類器不斷進行更新,在某種程度上對腦電信號的非平穩(wěn)變化具有一定的自適應性(通過實驗觀察,尚未經(jīng)過理論證明)。而一般系統(tǒng)在使用過程中,分類器不再進行更新。其性能由于腦電信號的非平穩(wěn)變化而降低,進而影響系統(tǒng)的性能。該系統(tǒng)利用了SUST-LSSVM算法在小樣本集下更高的分類準確率和SUST-ILDA算法極低的計算復雜度以及足量樣本下高的分類準確率。在實現(xiàn)上,采用雙分類器更新以及多進程和多線程的處理方式。
[Abstract]:Brain-computer interface (BCI) can directly establish communication mechanism between brain and external environment without relying on human external muscles and neural tissue, so it is mainly applied to the disabled with motor disorders and the functional assistance and rehabilitation of patients with neurological disorders.After decades of development BCI system framework and basic methods have been relatively perfect but there are still a large number of practical problems to be solved in the application field.Aiming at the problem of long training time in BCI system and the special requirement of high classification accuracy and real-time of online BCI system, this paper presents a semi-supervised linear discriminant analysis algorithm (-SUST-ILDAA) based on LDA algorithm.In order to reduce the training time before the use of the system, semi-supervised learning is adopted.First, the initial LDA classifier model is trained with a small number of labeled samples.After that, the self-training method is used to update the classifier step by step by using the unlabeled samples obtained online to improve the performance of the classifier. In order to reduce the computational complexity on line, an incremental update model based on LDA is derived.In theory, compared with the existing online semi-supervised learning algorithm SUST-LSSVM, the computational complexity is greatly reduced and stable.By using the data of the third BCI contest, it is proved that the proposed algorithm can achieve the classification accuracy similar to that of SUST-LSSVM with the increase of the number of online samples.And stable convergence. 2) designed and implemented a P300-based on-line semi-supervised character input brain computer interface system.Compared with the traditional P300 character input brain-computer interface system, the characteristic of this system is that the system only needs a short period of supervised training, then it can switch to input mode automatically.Greatly reduces the user before using the system monotonous training process.And with the input of characters, the classification accuracy of the system is improved continuously, reaching the stability of the system in the normal use phase, the online unlabeled samples are used to update the classifier continuously.To some extent, it has a certain adaptability to the non-stationary change of EEG signal (through experimental observation, it has not been proved by theory.In general, the classifier is no longer updated during the use of the system.Its performance is reduced because of the nonstationary change of EEG signal, which affects the performance of the system.The system makes use of the higher classification accuracy of SUST-LSSVM algorithm in small sample set, the lower computational complexity of SUST-ILDA algorithm and the higher classification accuracy of sufficient sample set.In the implementation, double classifier update and multi-process and multi-thread processing are adopted.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：R49;TP334.7

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本文編號：1740881