基于FPGA的實(shí)時腦機(jī)接口應(yīng)用研究
發(fā)布時間：2019-06-20 07:02
【摘要】：腦-機(jī)接口(Brain-computer Interface, BCI)是一種不依賴于腦的正常輸出通路的通訊系統(tǒng)。通過在人腦與計算機(jī)等電子設(shè)備之間建立直接通路，腦機(jī)接口系統(tǒng)可以把大腦發(fā)出的信息直接轉(zhuǎn)換成控制外部設(shè)備的命令，進(jìn)而代替人類肢體或語言器官的功能，以新的途徑實(shí)現(xiàn)人與外界的交流及對周圍環(huán)境的控制。腦機(jī)接口在康復(fù)醫(yī)學(xué)、工業(yè)、軍事等領(lǐng)域都有巨大的潛在應(yīng)用價值，已然逐漸成為一個研究熱點(diǎn)。然而，腦機(jī)接口技術(shù)目前仍在發(fā)展中，多數(shù)研究還處于實(shí)驗(yàn)室階段。 面對BCI技術(shù)發(fā)展的機(jī)遇與挑戰(zhàn)，本文開展了基于FPGA的實(shí)時腦機(jī)接口應(yīng)用研究。所構(gòu)建的BCI系統(tǒng)以瞬態(tài)視覺誘發(fā)電位為處理對象，相比于穩(wěn)態(tài)視覺誘發(fā)電位，瞬態(tài)視覺誘發(fā)電位易于檢測，而且低刺激頻率不易引起視覺疲勞。以FPGA開發(fā)板為核心處理平臺，，相比于單片機(jī)和DSP，F(xiàn)PGA在運(yùn)算速度和邏輯控制方面具有優(yōu)勢。 根據(jù)腦機(jī)接口應(yīng)用的要求，采用FPGA設(shè)計了新的視覺刺激器。每個刺激模塊都是黑白棋盤格交替閃爍的模式，不同點(diǎn)在于模塊上的標(biāo)志信息。在控制短消息發(fā)送的腦機(jī)接口應(yīng)用中，設(shè)計了兩個刺激界面，受試者首先選擇短信接收方，然后選擇短信內(nèi)容，用漢字標(biāo)注選項(xiàng)含義。在控制臺燈、風(fēng)扇運(yùn)行的腦機(jī)接口應(yīng)用中，刺激界面上的四個選項(xiàng)分別代表臺燈的點(diǎn)亮與熄滅、風(fēng)扇的轉(zhuǎn)動與停止，在刺激模塊上，用圖形形象地標(biāo)注各模塊所代表的選項(xiàng)。 BCI技術(shù)的研究重點(diǎn)是選擇合適的算法從強(qiáng)背景噪聲中提取視覺誘發(fā)電位，識別受試者的選擇。研究對比小波分解、主成分分析、K-近鄰法、BP神經(jīng)網(wǎng)絡(luò)等信號處理算法測試，最終選擇用db5小波對累加平均后的腦電信號進(jìn)行5尺度分解，提取D5、D4兩層細(xì)節(jié)系數(shù)作為特征向量，用BP神經(jīng)網(wǎng)絡(luò)識別，并用遺傳算法對BP網(wǎng)絡(luò)優(yōu)化。小波分解和BP網(wǎng)絡(luò)識別兩個處理步驟由Nios II系統(tǒng)實(shí)現(xiàn)。 本文將BCI系統(tǒng)用于控制TC35通訊模塊發(fā)送短消息。FPGA將視覺誘發(fā)電位識別結(jié)果轉(zhuǎn)換成發(fā)送短消息的命令，通過串口向TC35模塊發(fā)送AT指令，TC35向FPGA反饋指令處理信息，以此實(shí)現(xiàn)發(fā)送短消息的控制。在控制臺燈、風(fēng)扇運(yùn)行應(yīng)用中，F(xiàn)PGA將視覺誘發(fā)電位識別結(jié)果轉(zhuǎn)換成開關(guān)控制命令，通過控制繼電器的狀態(tài)實(shí)現(xiàn)對臺燈、風(fēng)扇的控制。 腦機(jī)接口實(shí)驗(yàn)表明，所選用算法具有較高的識別率，并且驗(yàn)證了用基于FPGA的實(shí)時腦機(jī)接口控制發(fā)送短消息和臺燈、風(fēng)扇的運(yùn)行具有可行性。
[Abstract]:Brain-computer interface (Brain-computer Interface, BCI) is a communication system which does not depend on the normal output pathway of the brain. By establishing a direct path between the human brain and the computer and other electronic devices, the brain-computer interface system can directly convert the information sent by the brain into the command to control the external equipment, and then replace the function of the human body or language organ, and realize the communication between man and the outside world and the control of the surrounding environment in a new way. Brain-computer interface has great potential application value in rehabilitation medicine, industry, military and other fields, and has gradually become a research focus. However, brain-computer interface technology is still in development, and most of the research is still in the laboratory stage. In the face of the opportunities and challenges of the development of BCI technology, the application of real-time brain-computer interface based on FPGA is studied in this paper. Compared with steady-state visual potentials, transient visual potentials are easy to detect and low stimulation frequency is not easy to cause visual fatigue in the constructed BCI system. Compared with single chip microcomputer and DSP,FPGA, FPGA development board has advantages in operation speed and logic control. According to the requirements of brain-computer interface application, a new visual stimulator is designed by using FPGA. Each stimulus module is a black and white chessboard alternately flashing mode, the difference lies in the logo information on the module. In the application of brain-computer interface to control the sending of short messages, two stimulation interfaces are designed. The subjects first select the receiver of the short message, then select the content of the short message, and mark the meaning of the option with Chinese characters. In the brain-computer interface application of console lamp and fan running, the four options on the stimulation interface represent the lighting and extinguishing of the lamp, the rotation and stop of the fan, and the options represented by each module are graphically marked on the stimulation module. The research focus of BCI technology is to select the appropriate algorithm to extract the visual evoked potential from the strong background noise and to identify the selection of the subjects. The signal processing algorithms such as wavelet decomposition, principal component analysis, K-nearest neighbor method and BP neural network are studied and compared. Finally, db5 wavelet is used to decompose the accumulated average EEG signals. D5, D4 two-layer detail coefficients are extracted as feature vectors, identified by BP neural network, and optimized by genetic algorithm. Wavelet decomposition and BP network recognition are implemented by Nios II system. In this paper, the BCI system is used to control the TC35 communication module to send the short message. FPGA converts the recognition result of visual evoked potential into the command to send the short message, sends the AT instruction to the TC35 module through the serial port, and the TC35 feedback the instruction processing information to the FPGA, so as to realize the control of sending the short message. In the operation application of console lamp and fan, FPGA converts the recognition result of visual evoked potential into switch control command, and realizes the control of table lamp and fan by controlling the state of relay. The brain-computer interface experiment shows that the selected algorithm has a high recognition rate, and verifies the feasibility of using the real-time brain-computer interface based on FPGA to control the transmission of short messages and lamp, and the operation of the fan.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R318
本文編號：2503025
[Abstract]:Brain-computer interface (Brain-computer Interface, BCI) is a communication system which does not depend on the normal output pathway of the brain. By establishing a direct path between the human brain and the computer and other electronic devices, the brain-computer interface system can directly convert the information sent by the brain into the command to control the external equipment, and then replace the function of the human body or language organ, and realize the communication between man and the outside world and the control of the surrounding environment in a new way. Brain-computer interface has great potential application value in rehabilitation medicine, industry, military and other fields, and has gradually become a research focus. However, brain-computer interface technology is still in development, and most of the research is still in the laboratory stage. In the face of the opportunities and challenges of the development of BCI technology, the application of real-time brain-computer interface based on FPGA is studied in this paper. Compared with steady-state visual potentials, transient visual potentials are easy to detect and low stimulation frequency is not easy to cause visual fatigue in the constructed BCI system. Compared with single chip microcomputer and DSP,FPGA, FPGA development board has advantages in operation speed and logic control. According to the requirements of brain-computer interface application, a new visual stimulator is designed by using FPGA. Each stimulus module is a black and white chessboard alternately flashing mode, the difference lies in the logo information on the module. In the application of brain-computer interface to control the sending of short messages, two stimulation interfaces are designed. The subjects first select the receiver of the short message, then select the content of the short message, and mark the meaning of the option with Chinese characters. In the brain-computer interface application of console lamp and fan running, the four options on the stimulation interface represent the lighting and extinguishing of the lamp, the rotation and stop of the fan, and the options represented by each module are graphically marked on the stimulation module. The research focus of BCI technology is to select the appropriate algorithm to extract the visual evoked potential from the strong background noise and to identify the selection of the subjects. The signal processing algorithms such as wavelet decomposition, principal component analysis, K-nearest neighbor method and BP neural network are studied and compared. Finally, db5 wavelet is used to decompose the accumulated average EEG signals. D5, D4 two-layer detail coefficients are extracted as feature vectors, identified by BP neural network, and optimized by genetic algorithm. Wavelet decomposition and BP network recognition are implemented by Nios II system. In this paper, the BCI system is used to control the TC35 communication module to send the short message. FPGA converts the recognition result of visual evoked potential into the command to send the short message, sends the AT instruction to the TC35 module through the serial port, and the TC35 feedback the instruction processing information to the FPGA, so as to realize the control of sending the short message. In the operation application of console lamp and fan, FPGA converts the recognition result of visual evoked potential into switch control command, and realizes the control of table lamp and fan by controlling the state of relay. The brain-computer interface experiment shows that the selected algorithm has a high recognition rate, and verifies the feasibility of using the real-time brain-computer interface based on FPGA to control the transmission of short messages and lamp, and the operation of the fan.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R318
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