本文關(guān)鍵詞：語言任務(wù)下腦電時頻網(wǎng)絡(luò)特征提取及在腦機接口中的應(yīng)用　出處：《河北工業(yè)大學(xué)》2016年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 腦機接口 皮層腦電 時頻特征 語言區(qū)定位 腦網(wǎng)絡(luò)

【摘要】：探索智能、意識的人腦機理,認識人的行為和情感,創(chuàng)新腦疾病診斷與治療,以及類腦計算和智能機器人是二十一世紀科學(xué)的前沿領(lǐng)域。腦機接口和腦網(wǎng)絡(luò)研究是該領(lǐng)域的重要研究內(nèi)容。本文圍繞人腦聽覺腦電事件相關(guān)電位和語言任務(wù)信息加工的時頻特征分析的科學(xué)問題,以腦機接口和腦網(wǎng)絡(luò)技術(shù)為手段,將腦皮層標準電極和微電極相結(jié)合,以臨床實驗室數(shù)據(jù)為依據(jù),系統(tǒng)研究了聽覺認知和語言任務(wù)下腦電時頻網(wǎng)絡(luò)特征提取與分類方法,構(gòu)建了動態(tài)的因效腦功能網(wǎng)絡(luò),分析了相關(guān)時頻特征及網(wǎng)絡(luò)性能,探索了大腦對聽覺及語言信息加工的特點、動態(tài)處理過程及表征方法,為基于聽覺與語言任務(wù)的腦機接口和腦網(wǎng)絡(luò)研究提供了依據(jù),對腦科學(xué)和認知神經(jīng)科學(xué)研究具有重要的參考價值。主要的創(chuàng)新性研究工作有:1、在聽覺事件相關(guān)電位的單次提取中,采用經(jīng)驗?zāi)B(tài)分解方法,并引入聽覺腦電特征信號N2ac,有效地提高了信號分類精度和傳輸速率。設(shè)計聽覺oddball刺激范式的腦機接口實驗,采用經(jīng)驗?zāi)B(tài)分解及相關(guān)系數(shù)進行特征提取,并在傳統(tǒng)事件相關(guān)電位N200、P300的基礎(chǔ)上,引入N2ac信號進行二分類研究。結(jié)果表明經(jīng)驗?zāi)B(tài)分解能夠有效提取單次實驗事件相關(guān)電位;事件相關(guān)電位N2ac的引入,可以有效提高分類精度。2、利用腦皮層標準電極和微電極,將時域分析和頻域分析相結(jié)合,研究高頻(High gamma)特征信號在語言任務(wù)下腦功能的定位作用。采集被試音節(jié)朗讀任務(wù)時的皮層腦電,比較多頻段腦電的時頻功率譜,探索了微電極與標準電極的激活狀態(tài),并與臨床皮層電刺激結(jié)果做比較,結(jié)果表明發(fā)音前后High gamma(70-110Hz)功率顯著升高,且可以用于語言區(qū)定位,并輔助臨床癲癇手術(shù)的術(shù)前評估。3、對比分析了皮層腦電標準電極和微電極空間分辨率對分類結(jié)果的影響。提取被試進行音節(jié)朗讀任務(wù)時,標準電極與微電極的High gamma頻段幅值特征,比較標準電極、微電極以及二者結(jié)合時的分類正確率,結(jié)果發(fā)現(xiàn)微電極雖然空間分辨率高,但是電極位置會影響分類結(jié)果;兩種電極的結(jié)合,能夠獲取全局以及局部腦電特征,可以顯著提高分類正確率。4、基于復(fù)雜網(wǎng)絡(luò)構(gòu)建和分析方法,探索語言加工過程中腦信息流的動態(tài)處理過程,并對腦網(wǎng)絡(luò)性能進行分析和評估。采用多尺度的皮層腦電,構(gòu)建音節(jié)朗讀任務(wù)時大腦語言區(qū)的時變動態(tài)有向網(wǎng)絡(luò)連接,同時采用度中心度和特征向量中心度的方法衡量網(wǎng)絡(luò)中節(jié)點的重要性,進一步輔助臨床癲癇手術(shù)的術(shù)前評估。5、分別采用互相關(guān)和時變動態(tài)貝葉斯網(wǎng)絡(luò)分析方法,對比分析網(wǎng)絡(luò)連接特征在腦機接口中的應(yīng)用。采用互相關(guān)和時變動態(tài)貝葉斯網(wǎng)絡(luò),分別衡量發(fā)音前后腦網(wǎng)絡(luò)的功能連接與效應(yīng)連接,進而采用網(wǎng)絡(luò)連接特征進行分類研究,并與High gamma特征的分類結(jié)果進行對比。結(jié)果顯示多數(shù)分類情況下時變動態(tài)貝葉斯網(wǎng)絡(luò)連接系數(shù)的分類結(jié)果與High gamma的分類結(jié)果相一致,時變動態(tài)貝葉斯網(wǎng)絡(luò)連接系數(shù)的分類結(jié)果要優(yōu)于互相關(guān)系數(shù)。
[Abstract]:Exploring the brain mechanism of intelligence and consciousness, recognizing human behaviors and emotions, innovating the diagnosis and treatment of brain diseases, and brain like computing and intelligent robots are the frontiers of Science in the twenty-first Century. The research of brain computer interface and brain network is an important research content in this field. The analysis of time-frequency characteristics of scientific problems on human auditory event-related potentials and language information processing task, the brain computer interface and brain network technology, the cerebral cortex and the combination of standard electrode microelectrode, clinical laboratory data, the system of network frequency feature extraction of auditory cognitive and language tasks EEG and classification method, constructs the dynamic effect due to brain functional network, time-frequency characteristics and network performance analysis, to explore the brain on auditory and language information processing characteristics, dynamic process and characterization methods, provide the basis for the hearing and language tasks in BCI and brain research based on network that has an important reference value for brain science and cognitive neuroscience. The main innovative research works are as follows: 1. In the single extraction of auditory event-related potentials, the empirical mode decomposition method is introduced, and the auditory EEG characteristic signal N2ac is introduced, which effectively improves the classification accuracy and transmission speed of signals. The brain computer interface experiment of auditory oddball stimulation paradigm was designed, and EMD and correlation coefficients were used to extract feature. Based on traditional event related potentials N200 and P300, N2ac signals were introduced to study two classifications. The results show that the empirical mode decomposition can effectively extract the related potential of the single experiment event, and the introduction of event related potential N2ac can effectively improve the classification accuracy. 2, using cortical standard electrodes and microelectrodes, we combine time domain analysis and frequency domain analysis to study the location function of high-frequency (High gamma) characteristic signals in language tasks. Subjects read syllable tasks in the cortical EEG, when more EEG frequency power spectrum, to explore the activation state of microelectrode and standard electrode, and compared with clinical cortical stimulation results, results show that the pronunciation of High before and after gamma (70-110Hz) power increased significantly, and can be used to locate the language areas, evaluation and assist clinical epilepsy surgery before operation. 3. The influence of cortical electroencephalogram (EEG) electrode and microelectrode spatial resolution on the classification results was compared and analyzed. Extraction test syllable aloud task, High gamma frequency amplitude characteristics of standard electrode and the microelectrode, compared to standard microelectrode and the combination of the two electrode, when the correct classification rate, results showed that although the microelectrode of high spatial resolution, but the electrode position will affect the classification results; a combination of two electrodes, can obtain the global and local EEG the characteristics, can significantly improve the rate of correct classification. 4, based on complex network construction and analysis method, we explored the dynamic process of brain information flow in language processing, and analyzed and evaluated the performance of brain network. A multi-scale cortical EEG is used to construct the temporal and dynamic network connectivity of the brain in the syllable reading task. Meanwhile, the importance of the nodes in the network is measured by the method of degree centrality and eigenvector centrality, which further assists the preoperative evaluation of clinical epileptic surgery. 5. Using the mutual correlation and time-varying dynamic Bayesian network analysis method, the application of network connection features in the brain machine interface is compared and analyzed. Cross correlation and time-varying dynamic Bayesian networks are used to measure the functional connectivity and effect connection of brain networks before and after pronunciations, respectively, and then classify them based on the characteristics of network connection, and compare them with the classification results of High gamma features. The results show that the classification results of time-varying dynamic Bayesian network connection coefficients are consistent with those of High gamma classification in most classifications. The classification results of time-varying dynamic Bayesian network connection coefficients are better than those of correlation numbers.
【學(xué)位授予單位】：河北工業(yè)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：R318;TN911.7

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