北京生物醫(yī)學(xué)工程

一種個性化動態(tài)腦功能網(wǎng)絡(luò)的構(gòu)建與特征提取方法

A personalized dynamic brain functional network and feature extraction

作者：張娜孫炎珺李明愛

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

關(guān)鍵詞：運動想象腦電信號; 動態(tài)腦功能網(wǎng)絡(luò); 特征提取; 個性化皮爾遜相關(guān)系數(shù); 共空間模式

分類號：R318.04

出版年·卷·期（頁碼）：2020·39·6（551-560）

摘要：

目的為了研究運動想象過程中腦功能網(wǎng)絡(luò)(Brain Functional Network, BFN)的時頻變化特征及對運動想象任務(wù)識別的影響，本文提出一種個性化皮爾遜相關(guān)系數(shù)(Personalized Pearson Correlation Coefficient, PPCC)及動態(tài)BFN的構(gòu)建與特征提取方法。方法首先，對各受試者運動想象腦電（Motor Imagery EEG, MI-EEG）頻帶范圍進(jìn)行兩級篩選，獲得其最優(yōu)頻帶；然后，將運動想象時間段進(jìn)行分割，計算各子時段最優(yōu)頻帶MI-EEG的PPCC，并用于構(gòu)建個性化的動態(tài)BFN；進(jìn)而，計算各個BFN的度作為網(wǎng)絡(luò)特征，并將多時段的網(wǎng)絡(luò)特征串行融合獲得特征向量；最后，針對BCI Competition III Data Set IIIa和BCI Competition IV Data Set 2a數(shù)據(jù)集，采用支持向量機檢驗特征的有效性。結(jié)果在兩個公共數(shù)據(jù)集上，本文方法的10×10折交叉驗證最高識別率分別為100.00%和68.84%。與基于共空間模式和基于PCC的BFN特征提取方法相比，具有明顯的優(yōu)勢，雙樣本t檢驗的結(jié)果也充分表明了PPCC的優(yōu)越性。結(jié)論與PCC相比，基于PPCC能構(gòu)建出可客觀地展現(xiàn)運動想象個性化特點的動態(tài)BFN，反映了不同受試者運動想象時大腦激活的差異性，及其在時域和頻域同時呈現(xiàn)的動態(tài)變化特點，有效增強了特征提取的自適應(yīng)性。

Objective In order to study the time-frequency variation characteristics of Brain Functional Network (BFN) in the process of motor imagery (MI) and the impact on the recognition of MI tasks, this paper proposes a Personalized Pearson Correlation Coefficient (PPCC) and dynamic BFN and feature extraction method. Methods First, two-stage screening is performed on each subject's Motor Imagery EEG (MI-EEG) frequency band range to obtain the optimum frequency band; Then，the MI time period is segmented into several sub-periods，the PPCC of the optimum frequency band MI-EEG is calculated for each sub-period, and personalized dynamic BFNs are constructed based on the PPCC values; Furthermore, the degree of each BFN is calculated as network features, which are serially fused to obtain feature vectors；Finally，the feature vectors are evaluated by support vector machine on the BCI Competition III Data Set IIIa and BCI Competition IV Data Set 2a. Results On the two public datasets, the 10×10 fold highest recognition rates achieve 100.00% and 68.84%, which have obvious advantages compared with common spatial pattern-based and PCC-based BFN feature extraction methods. The two-sample t-test clearly shows its superiority as well. Conclusions Compared with PCC, the dynamic BFNs which objectively express the personalized characteristic of MI-EEG can be constructed based on PPCC, reflect the extinctions of activated brain areas caused by MI among different subjects as well as the characteristics of dynamic changes exhibited in time and frequency domains simultaneously, and effectively enhance the adaptivity of feature extraction.

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