發(fā)布時間：2018-08-19 18:25

【摘要】：腦機接口是指不依賴于常規(guī)的脊髓或者外周神經(jīng)肌肉系統(tǒng),在腦與外部設(shè)備之間建立一種新型的信息交流與控制通道,從而實現(xiàn)腦與外界的直接交互。目前,非人靈長類獼猴是腦機接口研究的主要動物模型。在以獼猴為模型的腦機接口研究中,關(guān)鍵難點是如何長期獲得高質(zhì)量的神經(jīng)信號和運動信號,并通過構(gòu)建解碼算法實現(xiàn)神經(jīng)信號對運動信號的解碼預(yù)測。本文以非人靈長類獼猴為實驗對象,對初級運動皮層(primary motor cortex, M1區(qū))神經(jīng)集群信號記錄和腕部精細(xì)運動信號記錄等方面的若干關(guān)鍵技術(shù)進行了探索性研究,構(gòu)建了一個基于獼猴腕部運動的植入式腦機接口系統(tǒng)。通過對M1區(qū)神經(jīng)信號的處理和分析,實現(xiàn)了對腕部精細(xì)運動的解碼預(yù)測。本文主要研究內(nèi)容包括： 首先,本文設(shè)計了一個腕部運動信號采集系統(tǒng),應(yīng)用于猴子腕部內(nèi)翻、外翻、前展、后屈運動時運動參數(shù)的采集。該系統(tǒng)包括搖桿系統(tǒng)、固定系統(tǒng)、獎賞系統(tǒng)、PC (personal computer)機系統(tǒng)、下位機微控制器系統(tǒng)、攝像監(jiān)控系統(tǒng)等。針對猴子腕部精細(xì)運動的特點,本文設(shè)計了簡單的四方向center-out實驗范式。經(jīng)過任務(wù)分解訓(xùn)練,猴子可以較快地學(xué)會用腕部完成搖桿行為任務(wù)。該系統(tǒng)成功實現(xiàn)了猴子腕部運動信號的采集與記錄。 第二,本文對獼猴大腦皮層神經(jīng)集群記錄的若干關(guān)鍵技術(shù)進行了探索性研究。設(shè)計了兩種用于猴子頭部固定的機械裝置。比較分析了這兩種裝置的性能,發(fā)現(xiàn)八腳headpost和球頭型head holder可以較好地用于猴子頭部的固定。探索了兩種接口猶他電極的植入技術(shù),比較分析了兩種接口猶他電極的優(yōu)缺點。設(shè)計并改進了用于ICS-96接口猶他電極的固定基座,實現(xiàn)了猶他電極在M1區(qū)的精確埋植�；�于本文建立的微電極陣列植入技術(shù),能夠從猴子運動皮層長期獲得高質(zhì)量的神經(jīng)集群鋒電位信號(spike)。 第三,同步采集獲得猴子M1區(qū)的spike信號和腕部的運動信號,并定性分析了兩者之間的相關(guān)性。首先,探索了神經(jīng)集群發(fā)放模式與腕部運動方向之間的關(guān)系,發(fā)現(xiàn)神經(jīng)集群的spike發(fā)放率在搖桿運動起始前后的變化最大。神經(jīng)集群發(fā)放模式在不同搖桿方向上的差異性較大,這種差異性可用于不同搖桿“方向?qū)Α遍g的區(qū)分。其次,通過神經(jīng)可視化算法分析神經(jīng)信號與運動軌跡之間的關(guān)系,發(fā)現(xiàn)神經(jīng)信號具有很強的內(nèi)在規(guī)律性。降維后所得到的神經(jīng)軌跡具有明顯的可區(qū)分性,能夠很好地反映實際運動規(guī)律。 最后,以神經(jīng)元spike發(fā)放率為輸入特征,通過解碼算法實現(xiàn)了對猴子腕部運動方向、位置、速度的精確預(yù)測。此外,本文系統(tǒng)分析了影響解碼效果的各種因素。本文分別選用K最近鄰域算法(k-nearest neighbor algorithm,KNN)和支持向量機算法(support vector machine, SVM),實現(xiàn)了對猴子腕部運動方向的解碼預(yù)測,預(yù)測正確率可以達到96%。此外,本文選用的卡爾曼濾波算法(Kalman Filter, KF)和廣義回歸神經(jīng)網(wǎng)絡(luò)算法(general regression neural network, GRNN)對位置和速度等運動參數(shù)進行解碼分析。兩種算法均取得了較好的解碼效果。其中,GRNN算法對X、Y方向位置和速度的最高解碼相關(guān)系數(shù)(correlation coefficient,CC)值可以達到0.9170±0.0458,0.8872±0.0778,0.8254±0.0798和0.8376±0.0915。 綜上所述,利用猴子M1區(qū)記錄的神經(jīng)信號可以較好地預(yù)測出腕部的運動參數(shù)。本文建立的基于猴子M1區(qū)的腕部解碼系統(tǒng)是一個成功的腦機接口系統(tǒng)。該植入式腦機接口系統(tǒng)為進一步研究大腦運動皮層的編解碼規(guī)律,以及理解大腦控制運動的神經(jīng)生物學(xué)機制奠定了基礎(chǔ)。
[Abstract]:Brain-computer interface (BCI) refers to the establishment of a new type of communication and control channel between the brain and external devices, which is independent of the conventional spinal cord or peripheral neuromuscular system, so as to achieve direct interaction between the brain and the outside world. The key problem is how to obtain high-quality neural and motor signals for a long time, and how to decode and predict them by constructing decoding algorithms. Some key techniques such as motion signal recording have been explored and an implantable brain-computer interface system based on rhesus monkey wrist movement has been constructed.
Firstly, this paper designs a wrist motion signal acquisition system, which is used to collect the movement parameters of monkey wrist during varus, valgus, forward and backward movement.The system includes rocker system, fixed system, reward system, PC (personal computer) system, subordinate computer microcontroller system, video surveillance system, etc. After task decomposition training, monkeys can learn to use the wrist to complete the rocker behavior task quickly. The system successfully achieves the collection and recording of the wrist movement signals of monkeys.
Secondly, some key techniques of recording cerebral cortical neurons in rhesus monkeys were explored in this paper. Two kinds of mechanical devices were designed to fix the head of monkeys. The advantages and disadvantages of the two kinds of interface Utah electrodes are compared and analyzed. The fixed base for ICS-96 interface Utah electrodes is designed and improved, and the precise implantation of Utah electrodes in M1 region is realized. Based on the microelectrode array implantation technique, high quality nerves can be obtained from the motor cortex of monkeys for a long time. Cluster spike signal (spike).
Thirdly, the spike signals in M1 region and the wrist motion signals were collected synchronously, and the correlation between them was analyzed qualitatively. Firstly, the relationship between the firing pattern of nerve clusters and the direction of wrist movement was explored. It was found that the spike firing rate of nerve clusters changed most before and after the beginning of rocker movement. Secondly, the relationship between neural signals and locus of motion is analyzed by neural visualization algorithm, and it is found that neural signals have strong inherent regularity. The neural locus obtained after dimension reduction has obvious distinguishability. It can reflect the actual movement law very well.
Finally, the accurate prediction of the direction, position and speed of the monkey's wrist movement is realized by decoding algorithm with the spike firing rate of neurons as input feature. In addition, various factors affecting the decoding effect are analyzed systematically. In this paper, K-nearest neighbor algorithm (KNN) and support vector machine algorithm (SVM) are selected respectively. Vector machine (SVM) can decode and predict the movement direction of monkey's wrist, and the prediction accuracy can reach 96%. In addition, Kalman Filter (KF) and General Regression Neural Network (GRNN) are used to decode and analyze the motion parameters such as position and speed. The maximum decoding correlation coefficients (CC) of the position and speed in X and Y directions can reach 0.9170 (+ 0.0458), 0.8872 (+ 0.0778), 0.8254 (+ 0.0798) and 0.8376 (+ 0.0915).
In summary, the wrist motion parameters can be well predicted by using the neural signals recorded in the M1 region of the monkey. The wrist decoding system based on the M1 region of the monkey is a successful BCI system. The neurobiological mechanism of action laid the foundation.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2012
【分類號】：TN911.7;R318.04

【引證文獻】

相關(guān)博士學(xué)位論文 前1條

1 廖玉璽;植入式腦機接口神經(jīng)元鋒電位的時變特征分析與解碼研究[D];浙江大學(xué);2014年

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本文編號：2192462