基于CSP和ICA的多任務(wù)腦機(jī)接口分類方法比較研究
發(fā)布時間：2019-04-26 06:18
【摘要】：特征提取和模式分類是BCI系統(tǒng)最重要的兩個環(huán)節(jié),直接關(guān)系到BCI系統(tǒng)的分類識別率和分類穩(wěn)健性。本文主要研究內(nèi)容包括兩種特征提取算法——共空間模式(Common Spatial Pattern,CSP)和獨立分量分析(Independent Component Analysis,ICA),以及三種分類方法——Fisher判別分析FDA、支持向量機(jī)SVM以及KNN近鄰法。 在兩分類任務(wù)的腦電信號特征提取方面,CSP的效果非常好,但是在處理多分類數(shù)據(jù)時,必須將二進(jìn)制的CSP算法擴(kuò)展到多類條件。本文使用基于近似聯(lián)合對角化的多類CSP方法對腦電數(shù)據(jù)進(jìn)行特征提取；為了比較特征提取算法的性能,本文還使用了一種經(jīng)典的特征提取算法——獨立分量分析來提取腦電信號的特征。然后使用三種分類算法,即Fisher判別分析、支持向量機(jī)SVM以及KNN近鄰法,對提取的腦電特征信號進(jìn)行分類。 本文使用5個受試者的多任務(wù)腦電數(shù)據(jù),對這兩種特征提取算法與這三種分類方法進(jìn)行了仿真實驗,通過實驗比較和分析了它們性能。
[Abstract]:Feature extraction and pattern classification are two of the most important links in BCI system, which are directly related to the classification recognition rate and classification robustness of BCI system. The main contents of this paper include two feature extraction algorithms-(Common Spatial Pattern,CSP (Common Space Model) and (Independent Component Analysis,ICA (Independent component Analysis), as well as three classification methods-FDA, support Vector Machine (SVM) and KNN nearest neighbor method (KNN) based on Fisher discriminant analysis. In the aspect of feature extraction of EEG signals from two classification tasks, CSP has a very good effect, but the binary CSP algorithm must be extended to multi-class conditions when dealing with multi-classification data. In this paper, a multi-class CSP method based on approximate joint diagonalization is used to extract the features of EEG data. In order to compare the performance of the feature extraction algorithm, a classical feature extraction algorithm, Independent component Analysis (ICA), is used to extract the features of EEG signals. Then three classification algorithms, namely Fisher discriminant analysis, support vector machine SVM and KNN nearest neighbor method, are used to classify the extracted EEG feature signals. In this paper, the multi-task EEG data of five subjects are used to simulate the two feature extraction algorithms and these three classification methods, and their performance is compared and analyzed by experiments.
【學(xué)位授予單位】：南昌大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TP334.7
本文編號：2465827
[Abstract]:Feature extraction and pattern classification are two of the most important links in BCI system, which are directly related to the classification recognition rate and classification robustness of BCI system. The main contents of this paper include two feature extraction algorithms-(Common Spatial Pattern,CSP (Common Space Model) and (Independent Component Analysis,ICA (Independent component Analysis), as well as three classification methods-FDA, support Vector Machine (SVM) and KNN nearest neighbor method (KNN) based on Fisher discriminant analysis. In the aspect of feature extraction of EEG signals from two classification tasks, CSP has a very good effect, but the binary CSP algorithm must be extended to multi-class conditions when dealing with multi-classification data. In this paper, a multi-class CSP method based on approximate joint diagonalization is used to extract the features of EEG data. In order to compare the performance of the feature extraction algorithm, a classical feature extraction algorithm, Independent component Analysis (ICA), is used to extract the features of EEG signals. Then three classification algorithms, namely Fisher discriminant analysis, support vector machine SVM and KNN nearest neighbor method, are used to classify the extracted EEG feature signals. In this paper, the multi-task EEG data of five subjects are used to simulate the two feature extraction algorithms and these three classification methods, and their performance is compared and analyzed by experiments.
【學(xué)位授予單位】：南昌大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：TP334.7
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