本文選題：P300腦機接口 + 字符輸入�。� 參考：《華南理工大學(xué)》2014年碩士論文

【摘要】：在人工智能、模式識別和信號處理等領(lǐng)域，腦機接口(Brain-computer interface，BCI)技術(shù)逐漸成為備受關(guān)注的課題，并對失去肢體運動能力的殘疾人產(chǎn)生了突出作用。目前，該技術(shù)雖然逐漸應(yīng)用于醫(yī)學(xué)和娛樂等各個領(lǐng)域，但仍存在系統(tǒng)通訊速率較低、訓(xùn)練時間較長等問題。針對上述問題，本文主要做了以下兩個方面的研究工作： 首先，本文設(shè)計了一種改進的基于P300的字符輸入BCI系統(tǒng)，該系統(tǒng)采用的方法是根據(jù)受試者的實時腦電數(shù)據(jù)特征，計算出可能的目標(biāo)與非目標(biāo)字符，據(jù)此屏蔽這些非目標(biāo)字符的閃爍，從而縮短閃爍字符序列，并減弱非目標(biāo)字符閃爍對受試者的干擾。通過采集多位受試者的腦電數(shù)據(jù)進行分析，實驗結(jié)果表明，該方法在保持準(zhǔn)確率基本沒有下降的同時，提高了字符輸入的速度，從而提高了系統(tǒng)的信息傳輸率（Information transferrate，ITR），有利于解決系統(tǒng)的實用性問題。 其次，針對訓(xùn)練時間過長的問題，，可以通過半監(jiān)督學(xué)習(xí)減少采集有標(biāo)記樣本的時間，我們研究P300BCI系統(tǒng)的在線半監(jiān)督學(xué)習(xí)。通常，半監(jiān)督學(xué)習(xí)是先采集少量有標(biāo)記的數(shù)據(jù)進行初始模型訓(xùn)練，再利用相對較多的未標(biāo)記數(shù)據(jù)進行模型更新。而本文采用的方法是在上述初始模型訓(xùn)練之后，再利用在線獲取的未標(biāo)記數(shù)據(jù)來逐步更新模型。為了確保分類模型的可靠性，需要對未標(biāo)記的數(shù)據(jù)進行樣本選擇，以降低異常數(shù)據(jù)對模型的干擾。本文采用的樣本選擇方法是根據(jù)分類模型計算出每個未標(biāo)記數(shù)據(jù)的分類器響應(yīng)值來判定此數(shù)據(jù)的可信度，即篩選出有利于模型更新的未標(biāo)記數(shù)據(jù)來逐步優(yōu)化分類模型。實驗中對在線半監(jiān)督學(xué)習(xí)的BCI系統(tǒng)在使用樣本選擇方法前后進行比較，并分別對采集到的在線數(shù)據(jù)進行離線分析。實驗結(jié)果表明，在在線半監(jiān)督學(xué)習(xí)的P300BCI系統(tǒng)中使用樣本選擇方法，不僅能夠減少訓(xùn)練時間，而且能夠在有標(biāo)記樣本較少的情況下提高分類準(zhǔn)確率。
[Abstract]:In the fields of artificial intelligence, pattern recognition and signal processing, Brain-Computer Interface (BCI) technology has gradually become a subject of great concern, and has played an important role in the disabled who have lost the ability of limb movement. At present, although the technology has been gradually applied in various fields such as medicine and entertainment, there are still some problems such as low communication rate and long training time. In view of the above problems, this paper mainly does the following two aspects of research work: Firstly, an improved P300-based character input BCI system is designed. The method of the system is to calculate the possible target and non-target characters according to the real time EEG data characteristics of the subjects. Therefore, the flicker of these non-target characters can be shielded so as to shorten the sequence of scintillation characters and reduce the interference of non-target character flicker to the subjects. By collecting EEG data of many subjects for analysis, the experimental results show that the method can improve the speed of character input while keeping the accuracy rate unchanged. Thus, the information transfer rate of the system is improved and the practical problem of the system is solved. Secondly, aiming at the problem of long training time, we can reduce the time of collecting labeled samples by semi-supervised learning. We study the online semi-supervised learning in P300BCI system. Usually, semi-supervised learning first collects a small amount of labeled data for initial model training, and then uses relatively more unlabeled data to update the model. The method used in this paper is to update the model step by step by using the unlabeled data obtained online after the initial model training. In order to ensure the reliability of the classification model, it is necessary to select samples from unlabeled data to reduce the interference of abnormal data to the model. The sample selection method used in this paper is to calculate the classifier response value of each unlabeled data according to the classification model to determine the reliability of the data, that is, to select the unlabeled data to update the model to optimize the classification model step by step. In the experiment, the online semi-supervised learning BCI system is compared before and after using the sample selection method, and the collected on-line data are analyzed off-line respectively. The experimental results show that using the sample selection method in the online semi-supervised learning P300BCI system can not only reduce the training time, but also improve the classification accuracy when there are fewer labeled samples.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP334.7;TP18

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