本文選題：侵入式雙向腦機(jī)接口 + 電激勵��； 參考：《武漢大學(xué)》2016年博士論文

【摘要】：腦機(jī)接口系統(tǒng)依靠傳感器提取的神經(jīng)元活動信息為移動計算機(jī)終端和假肢等外部設(shè)備提供命令及控制信號。近年來,腦機(jī)接口作為非傳統(tǒng)的交流渠道為大腦與外部設(shè)備建立直接連接。腦機(jī)接口的應(yīng)用包括：(1)通過控制輔助設(shè)備幫助患者實現(xiàn)已喪失的交流能力和運動能力；(2)對某些患有特定疾病的患者進(jìn)行實時身體狀態(tài)監(jiān)控；(3)對患者在康復(fù)治療期間以及之后進(jìn)行實時情感狀態(tài)的監(jiān)控；(4)與運動功能相關(guān)的大腦功能復(fù)健等。腦機(jī)接口根據(jù)系統(tǒng)位置的不同分為侵入式腦機(jī)接口系統(tǒng)和非侵入式腦機(jī)接口系統(tǒng),由于侵入式系統(tǒng)獨特的系統(tǒng)環(huán)境和人體對外來設(shè)備的排異性,我們對侵入式雙向腦機(jī)接口系統(tǒng)的設(shè)計提出了嚴(yán)格的要求,并針對各個模塊進(jìn)行了詳細(xì)的探討與分析,提出了具體的設(shè)計指標(biāo),為后續(xù)設(shè)計并驗證生物信號采集系統(tǒng)的性能提供了理論基礎(chǔ)。在腦機(jī)接口的設(shè)計過程中,我們期望通過一個基礎(chǔ)平臺來整合不同的硬件系統(tǒng)和軟件系統(tǒng),這種選擇的多樣性和靈活性有效地降低了腦機(jī)接口的開發(fā)成本和研究門檻,增強(qiáng)了不同領(lǐng)域的合作研究機(jī)會。本文提出的一種模塊化無線腦機(jī)接口軟硬件系統(tǒng)能夠采集并傳輸32通道腦電信號或8通道肌電信號,同時可選擇其它生物信號作為輔助輸入信號,譬如運動傳感器數(shù)據(jù)和溫度傳感器數(shù)據(jù)等。該系統(tǒng)為研究者提供了一個低功耗的通信接口和組件化的軟硬件基礎(chǔ)框架,使得研究者可以根據(jù)自己的實際情況選擇最適合的軟硬件系統(tǒng)整合到基礎(chǔ)框架中。該系統(tǒng)已通過不同的組件配置測試,并取得可與其它醫(yī)療級腦電信號、肌電信號采集系統(tǒng)相媲美的數(shù)據(jù)結(jié)果。針對傳統(tǒng)的腦機(jī)接口系統(tǒng)只包含將采集的信號傳輸?shù)街鳈C(jī)設(shè)備端進(jìn)行信號處理的單方向功能的問題,設(shè)計了一個具有無線充電功能的雙向腦機(jī)接口系統(tǒng),分析并檢測集到的信號特征,進(jìn)而施加電流刺激信號反向作用于腦部或脊髓神經(jīng),用于治療某些中樞神經(jīng)系統(tǒng)的疾病,促進(jìn)神經(jīng)可塑性。通過實驗室臺架實驗和猴子體內(nèi)實驗進(jìn)行局部場電位信號采集,分析并驗證了系統(tǒng)性能,得出了對采集到的局部場電位信號進(jìn)行時域分析和時頻域分析,驗證了電流激勵信號對大腦感知運動皮層的影響。對于生物信號應(yīng)用不同的信號處理技術(shù)是揭示生物神經(jīng)生理背景的重要手段之一。本文設(shè)計的可穿戴肌電信號采集系統(tǒng)與市場上已有的高精度肌電信號采集系統(tǒng)相比,可獲得更出色的信號質(zhì)量及更穩(wěn)定的系統(tǒng)性能。通過提取前臂不同肌肉群的肌電信號對7組不同的前臂和手部動作進(jìn)行離線信號分類處理,獲得了較高的分類準(zhǔn)確率。近年來,深度學(xué)習(xí)算法在分析生物信號特征時也發(fā)揮了顯著的作用。本文將多個模型的深度信念網(wǎng)絡(luò)應(yīng)用在從上臂肱二頭肌提取的肌電信號上,分析并對肌肉的疲勞程度進(jìn)行分類。非侵入式腦電信號提取系統(tǒng)被用于與注意力集中程度相關(guān)聯(lián)的腦電α節(jié)律檢測。除了使用傳統(tǒng)的特征提取方法檢測注意力集中程度,本文提出續(xù)同源性優(yōu)化算法腦電信號中α節(jié)律代表的周期特征,作為輔助特征對有α節(jié)律出現(xiàn)的腦電數(shù)據(jù)和沒有α節(jié)律出現(xiàn)的腦電數(shù)據(jù)進(jìn)行分類。論文設(shè)計的腦機(jī)接口系統(tǒng)針對不同的系統(tǒng)設(shè)置進(jìn)行了功耗分析,通過與近年來研發(fā)的類似系統(tǒng)進(jìn)行比較,該系統(tǒng)具有較為先進(jìn)的系統(tǒng)性能,證明了該系統(tǒng)在促進(jìn)神經(jīng)可塑性領(lǐng)域具有很大潛力。
[Abstract]:The brain machine interface system relies on the neuron activity information extracted by the sensor to provide command and control signals for the external devices such as mobile computer terminals and artificial limbs. In recent years, the brain machine interface has been used as a non-traditional communication channel to establish direct connection between the brain and external devices. The application of the brain machine interface includes: (1) help the auxiliary equipment to help. The patient realized lost communication and exercise ability; (2) real-time physical state monitoring for certain patients with specific diseases; (3) monitoring the emotional state of the patients during and after rehabilitation treatment; (4) the brain function related to motor function could be rehabilitate. For the intrusive brain machine interface system and the non-invasive brain machine interface system, due to the unique system environment of the intrusive system and the human body's rejection of the external equipment, we put forward strict requirements for the design of the intrusive bidirectional brain machine interface system, and have carried out a detailed discussion and analysis of each module, and put forward the specific design. It provides a theoretical basis for subsequent design and verification of the performance of the biological signal acquisition system. In the design of the brain machine interface, we expect to integrate different hardware and software systems through a basic platform. The diversity and flexibility of this selection can effectively reduce the cost and threshold of the development of the brain machine interface. A modular wireless brain machine interface software and hardware system can collect and transmit 32 channels of EEG or 8 channel EMG signals, while other biological signals can be selected as auxiliary input signals, such as motion sensor data and temperature sensor data. The researchers provide a low power communication interface and a component-based software and hardware framework, so that the researchers can choose the most suitable software and hardware system to be integrated into the basic framework according to their own actual conditions. The system has been tested by different components, and can obtain the signal acquisition system with other medical level brain signals and electromyography. The traditional brain machine interface system contains only the single directional function of signal processing by transmitting the collected signals to the host device end. A two-way BCI system with wireless charging function is designed to analyze and detect the signal features set, and then apply the current stimulus to the reverse signal. The brain or spinal nerve is used to treat some diseases of the central nervous system and promote the plasticity of the nervous system. The local field potential signal is collected through laboratory bench test and monkey experiment, and the performance of the system is analyzed and verified. The time domain analysis and time frequency analysis of local field potential signal are obtained. The effect of current excitation signals on the cerebral cortex of the brain is verified. The application of different signal processing techniques to biological signals is one of the important means to reveal the biological background of biological neurophysiology. The wearable muscular electrical signal acquisition system designed in this paper can be better than the high-precision signal acquisition system in the market. Signal quality and more stable system performance. By extracting the EMG signals from different forearm muscles, 7 groups of different forearm and hand movements are classified and classified, and a higher classification accuracy is obtained. In recent years, the depth learning algorithm has also played a significant role in the analysis of the characteristics of biological signals. The deep belief network is applied to the electromyographic signal extracted from the biceps brachii muscle of the upper arm to analyze and classify the degree of muscle fatigue. The non invasive EEG extraction system is used to detect the alpha rhythms associated with the concentration of attention. In addition to using the traditional feature extraction method to detect the concentration of attention, this paper This paper presents the periodic characteristics of the alpha rhythm representation in the EEG, which is used as an auxiliary feature to classify the EEG data with alpha rhythm and the EEG data without alpha rhythm. The paper designed the brain machine interface system to analyze the power consumption for different system settings, through similar lines developed in recent years. By comparison, the system has more advanced system performance, which proves that the system has great potential in promoting neural plasticity.

【學(xué)位授予單位】：武漢大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：TN911.7;R318

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本文編號：1836584