北京生物醫(yī)學(xué)工程

基于鎖相值和圖論的腦功能網(wǎng)絡(luò)特征提取方法

Applying phase locking value and graph theory for feature extraction of brain functional network

作者：李明愛南琳孫炎珺

單位：北京工業(yè)大學(xué)信息學(xué)部(北京 100124)<p>計算智能與智能系統(tǒng)北京市重點實驗室(北京 100124)</p>

關(guān)鍵詞：運動想象腦電信號；腦功能網(wǎng)絡(luò)；圖論；特征提取；鎖相值

分類號：R318.04

出版年·卷·期（頁碼）：2019·38·1（15-21）

摘要：

目的為了研究大腦運動想象時腦功能網(wǎng)絡(luò)的狀態(tài)變化，并區(qū)分運動想象任務(wù)，本文提出一種基于鎖相值和圖論的腦功能網(wǎng)絡(luò)特征提取方法。方法獲取Mu節(jié)律和Beta節(jié)律的運動想象腦電信號(motor imagery electroencephalography, MI-EEG)，計算任意兩導(dǎo)相同節(jié)律MI-EEG之間的鎖相值，分別構(gòu)建兩個節(jié)律的腦功能網(wǎng)絡(luò)，并提取6種全局網(wǎng)絡(luò)特征參數(shù)，對其歸一化處理后進行串行融合獲得特征向量。最后以支持向量機作為分類器，采用10折交叉驗證法，在BCI Competition III Data Sets IIIa數(shù)據(jù)集上對兩種運動想象任務(wù)進行分類。結(jié)果相比于其他腦網(wǎng)絡(luò)特征提取方法，本文方法獲得了較高的識別率，最高識別率和平均識別率分別達到100.00%和83.33%。結(jié)論從腦功能網(wǎng)絡(luò)的角度，通過構(gòu)建Mu節(jié)律和Beta節(jié)律兩個運動節(jié)律MI-EEG的腦功能網(wǎng)絡(luò)，提取多個反映大腦網(wǎng)絡(luò)整體信息的特征，相對于構(gòu)建單一運動節(jié)律MI-EEG的腦功能網(wǎng)絡(luò)，提取單個網(wǎng)絡(luò)特征參數(shù)，能夠有效改善運動想象任務(wù)的識別效果，為MI-EEG信號的特征提取方法提供了一種新的思路。

Objective In order to study the state changes of brain functional network caused by motor imagery and distinguish the motor imagery tasks, a feature extraction method of brain functional network is proposed by using phase locking value and graph theory. Methods The motor imagery electroencephalography (MI-EEG) signals corresponding to Mu rhythm and Beta rhythm are obtained, and the phase locking values between any two channels are calculated on the same rhythm MI-EEG. Then, two brain functional networks of Mu rhythm and Beta rhythm are constructed respectively, and six global network parameters are extracted. After normalizing, these parameters are serially fused to construct feature vectors. Finally, support vector machine (SVM) is used as a classifier to verify the effectiveness of this method by using 10-fold cross validation on the public dataset: BCI Competition III Data Sets IIIa. Results Compared with other feature extraction methods of brain network, this proposed method achieves higher recognition rates, and the highest recognition rate and average recognition rate are 100.00% and 83.33%, respectively. Conclusions From the perspective of brain functional network, by constructing the brain functional networks of Mu rhythm and Beta rhythm MI-EEG, multiple network parameters that reflect the whole information of brain network are extracted. Compared with the method which builds one brain functional network using a single rhythm MI-EEG and extracts individual network parameter, this method can effectively improve the recognition effect of motor imagery tasks, and provides a new idea for the feature extraction method of MI-EEG signals.

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