本文關(guān)鍵詞：運(yùn)動(dòng)想象腦電信號(hào)的特征提取算法研究　出處：《安徽大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 腦-機(jī)接口 運(yùn)動(dòng)想象 包絡(luò)提取 空域?yàn)V波

【摘要】：腦-機(jī)接口(Brain-computer interface, BCI)是一種不依賴(lài)外周神經(jīng)和肌肉等傳統(tǒng)信息通道的特殊人-機(jī)交互技術(shù)。利用該技術(shù),可實(shí)現(xiàn)大腦與外部設(shè)備之間的直接通信和控制。作為神經(jīng)活動(dòng)的信息載體,頭皮腦電(EEG)信號(hào)能實(shí)時(shí)反映思維狀態(tài)的變化,并且容易檢測(cè),因此在非植入式BCI系統(tǒng)中得到了廣泛應(yīng)用。然而由于大腦容積傳導(dǎo)效應(yīng)的存在,使得頭皮腦電的空間分辨率較低。同時(shí),非神經(jīng)活動(dòng)偽跡(如眼電、肌電、心電等)和環(huán)境噪聲也大大降低了有用信息的信噪比。因此,在基于EEG的BCI系統(tǒng)實(shí)現(xiàn)研究中,如何從多道頭皮腦電中獲取思維相關(guān)的真實(shí)神經(jīng)活動(dòng)成分是非常關(guān)鍵的技術(shù)環(huán)節(jié)。 本文圍繞運(yùn)動(dòng)想象BCI系統(tǒng)的實(shí)現(xiàn),對(duì)EEG信號(hào)處理和特征提取新方法開(kāi)展研究,主要做了以下工作： (1)設(shè)計(jì)了運(yùn)動(dòng)想象BCI的實(shí)驗(yàn)范式,并采集了較豐富的運(yùn)行想象EEG數(shù)據(jù),為后續(xù)研究打下了良好的基礎(chǔ)。 (2)針對(duì)任務(wù)相關(guān)EEG節(jié)律波的包絡(luò)檢測(cè)和運(yùn)動(dòng)想象分類(lèi)問(wèn)題,實(shí)現(xiàn)了四種包絡(luò)檢測(cè)方法：非線(xiàn)性能量算子(Nonlinear energy operator, NEO)、希爾伯特變換(Hilbert transform, HT)和兩種滑動(dòng)窗獨(dú)立分量分析(Independent component analysis, ICA)算法�；贐CI2003競(jìng)賽數(shù)據(jù),對(duì)四種包絡(luò)檢測(cè)算法在運(yùn)動(dòng)想象分類(lèi)中的應(yīng)用效果進(jìn)行了分析和比較。研究了干擾偽跡對(duì)包絡(luò)檢測(cè)精度的影響,并提出了相應(yīng)的改進(jìn)思路。 (3)研究了結(jié)合時(shí)、頻、空域的空域?yàn)V波新方法。ICA和共同空間模式(Common spatial pattern, CSP)是兩種重要的空域?yàn)V波算法。兩種算法都是提取空域?yàn)V波器后對(duì)預(yù)處理后的腦電信號(hào)進(jìn)行濾波,得到與神經(jīng)活動(dòng)相關(guān)的隱含信號(hào)源。由于濾波器的設(shè)計(jì)原理的不同,最終所得隱含源的物理意義差別也很大。本文首先基于實(shí)測(cè)運(yùn)動(dòng)想象EEG數(shù)據(jù),分析和比較兩種空域?yàn)V波方法各自的性能特點(diǎn)。在此基礎(chǔ)上,給出了一種結(jié)合ICA和CSP的EEG特征提取新方法,實(shí)驗(yàn)結(jié)果驗(yàn)證了所提方法的有效性。
[Abstract]:Brain computer interface (Brain-computer interface BCI) is a special one - a traditional information channel is not dependent on the peripheral nerves and muscles of the machine interaction technology. Using this technology, can realize direct communication and control between brain and external devices. As the information carrier of neural activity, electroencephalography (EEG) signals the changes reflect the real state of mind, and can easily be detected, so it is widely used in non-invasive BCI system. However, the brain volume conduction effect, the spatial resolution of scalp EEG is low. At the same time, non neural activity artifacts (such as EOG, EMG, ECG etc) and environmental noise greatly reduce the useful information of the signal-to-noise ratio. Therefore, in the BCI implementation of EEG system based on the research, thinking how to get real neural activity in the correlated components from multichannel scalp EEG is very important in technology.
This paper focuses on the implementation of the motion picture BCI system, and studies the new methods of EEG signal processing and feature extraction. The following work is done:
(1) the experimental paradigm of motion imaginary BCI was designed, and the more abundant EEG data were collected, which laid a good foundation for the follow-up study.
(2) according to the envelope detection and motion tasks related to EEG rhythm wave imagery classification problem, realized four kinds of envelope detection methods: nonlinear energy operator (Nonlinear energy, operator, NEO), Hilbert (Hilbert transform, HT transform) and two kinds of sliding window independent component analysis (Independent component analysis, ICA BCI2003) algorithm. The competition based on the data of four kinds of envelope detection algorithms are analyzed and compared in the effect of motor imagery classification. Research on interference artifact effect on envelope detection accuracy, and proposes the corresponding improvement ideas.
(3) the combination of frequency,.ICA, a new method of spatial filtering and spatial common spatial pattern (Common spatial, pattern, CSP) are two important spatial filtering algorithm. The two algorithms are extracted from the spatial filter to filter the EEG signal preprocessing, associated with neural activity implied signal source. Due to the design principle of the filter, is also a great physical meaning resulting implicit difference. Source based on the EEG data measured imagine movement, performance analysis and comparison of two kinds of spatial filtering methods respectively. Based on this, a new method of combining ICA and CSP EEG feature extraction is presented, experimental the results verify the effectiveness of the proposed method.

【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN911.7

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