本文選題：腦-機接口 + 運動想象�。� 參考：《杭州電子科技大學(xué)》2016年碩士論文

【摘要】：腦-機接口技術(shù)直接從人體的思維源頭——大腦出發(fā),建立起連接大腦和計算機或其他設(shè)備間的“交流橋梁”,顛覆了原有的依賴外周神經(jīng)和肌肉組織的溝通方式。由于該技術(shù)在醫(yī)學(xué)、娛樂、智能生活等多個領(lǐng)域具有廣泛的應(yīng)用前景,所以成為腦科學(xué)研究的熱點之一。本文以多變量運動想象腦電信號為研究對象,對預(yù)處理、特征提取、模式分類等信號處理過程進行研究,并應(yīng)用于電動假肢的腦電控制。本文主要完成的研究工作以及取得的成果,如下:(1)預(yù)處理:為了減少噪聲信號的干擾,本文采用了兩種預(yù)處理方法:巴特沃斯濾波器和自適應(yīng)小波閾值消噪法。分別在2008年公開競賽數(shù)據(jù)集上進行實驗,結(jié)果說明前者可以獲取與運動想象節(jié)律信號相關(guān)的頻段信息,后者可以有效地降低噪聲干擾以提高信噪比。(2)特征提取:針對選取IMFs分量依賴于經(jīng)驗的問題,本文提出一種基于噪聲輔助多變量經(jīng)驗?zāi)Ｊ椒纸?NA-MEMD)和互信息的有用IMF識別方法,用于腦電特征提取。首先,使用NA-MEMD算法對多通道信號進行分解得到多尺度IMF分量。然后,采用互信息法分別計算各尺度上信號與其IMF分量、噪聲與其IMF分量、信號IMF分量與噪聲IMF分量之間的相關(guān)性,計算相應(yīng)的敏感因子以篩選出包含有用信息的IMF分量,將它們疊加起來得到各通道重構(gòu)信號,采用共同空間模式算法提取重構(gòu)信號的特征。該算法自適應(yīng)選取了與腦電信號相關(guān)的有用信息,提高了特征區(qū)分度。通過與其他選取方法對比,該算法有效性得到驗證。(3)模式分類:針對傳統(tǒng)的高斯過程采用共軛梯度法確定超參數(shù)時對初值有較強依賴性且易陷入局部最優(yōu)的問題,本文提出了一種基于人工蜂群優(yōu)化的高斯過程分類方法,用于腦電信號的模式識別。首先,構(gòu)建高斯過程模型,選擇合適的核函數(shù)且確定待優(yōu)化的參數(shù)。然后,選取識別錯誤率的倒數(shù)為適應(yīng)度函數(shù),使用人工蜂群算法搜索尋找出限定范圍內(nèi)可以取得最優(yōu)準(zhǔn)確率的超參數(shù)。最后,采用參數(shù)優(yōu)化后的高斯過程分類器對樣本分類,并通過實驗證明本文算法的有效性。(4)多變量運動想象腦電在電動假肢控制上的探究:首先介紹了實驗背景,接著設(shè)計了總體控制方案,然后設(shè)計了實驗范式并對腦電信號進行采集。隨后采用自適應(yīng)小波閾值消噪法對腦電信號進行預(yù)處理,使用NA-MEMD和互信息方法提取腦電信號的特征,運用基于人工蜂群優(yōu)化的高斯過程分類器對腦電特征進行分類。最后將分類結(jié)果映射成對應(yīng)的控制命令,驅(qū)動電動假肢完成握拳和展拳動作。
[Abstract]:The brain-computer interface technology starts directly from the brain, the source of human thinking, and establishes a "bridge of communication" between the brain and the computer or other devices, which subverts the way of communication that relies on peripheral nerve and muscle tissue. Because of its wide application prospect in medicine, entertainment, intelligent life and so on, this technology has become one of the hotspots in brain science research. In this paper, the signal processing processes such as preprocessing, feature extraction and pattern classification are studied, and applied to EEG control of electric prosthesis. In order to reduce the interference of noise signal, two preprocessing methods are adopted: Butterworth filter and adaptive wavelet threshold denoising method. Experiments were carried out on the open competition data set in 2008. The results show that the former can obtain the frequency band information related to the motion imagination rhythm signal. The latter can effectively reduce noise interference to improve signal-to-noise ratio (SNR) feature extraction. Aiming at the problem of selecting IMFs components depending on experience, this paper proposes a useful IMF recognition method based on noise-assisted multivariable empirical mode decomposition and mutual information. It is used for EEG feature extraction. Firstly, NA-MEMD algorithm is used to decompose multi-channel signals to obtain multi-scale IMF components. Then, the correlation between the signal and its IMF component, the noise and its IMF component, the signal IMF component and the noise IMF component are calculated by mutual information method, and the corresponding sensitivity factors are calculated to screen out the IMF components containing useful information. The reconstructed signals of each channel are obtained by stacking them together, and the features of the reconstructed signals are extracted by the common spatial pattern algorithm. The algorithm adaptively selects useful information related to EEG signals and improves the classification of features. By comparing with other selection methods, the validity of the algorithm is verified. The proposed method can be applied to the traditional Gao Si process where the conjugate gradient method is used to determine the superparameters, which is highly dependent on the initial value and easily falls into the local optimal condition. In this paper, a Gao Si process classification method based on artificial bee colony optimization is proposed for pattern recognition of EEG signals. Firstly, the Gao Si process model is constructed, the appropriate kernel function is selected and the parameters to be optimized are determined. Then, the inverse of the recognition error rate is selected as the fitness function, and the artificial bee colony algorithm is used to search for the super-parameters which can obtain the best accuracy in the limited range. Finally, the Gao Si process classifier with optimized parameters is used to classify the samples, and the experimental results show that the algorithm is effective and the multivariable motion imagination EEG is applied to the control of electric prosthesis. Firstly, the background of the experiment is introduced. Then the overall control scheme is designed, and then the experimental paradigm is designed and EEG signals are collected. Then the adaptive wavelet threshold de-noising method is used to pre-process EEG signals, NA-MEMD and mutual information methods are used to extract the features of EEG signals, and Gao Si process classifiers based on artificial bee colony optimization are used to classify EEG features. Finally, the classification results are mapped to the corresponding control commands, driving the electric prosthesis to complete the grip and swing.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TN911.6;R318

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