本文選題：腦-機(jī)接口　切入點(diǎn)：共空域模式　出處：《南昌大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：腦-機(jī)接口是一種能將大腦意識(shí)信號(hào)轉(zhuǎn)化成外部輸出命令的技術(shù)。在基于運(yùn)動(dòng)想象的腦-機(jī)接口系統(tǒng)中,共空域模式(Common Spatial Pattern, CSP)是一種成功的算法。它的優(yōu)勢(shì)在于能設(shè)計(jì)一種最優(yōu)的空域?yàn)V波器,通過空域?yàn)V波可以提取腦電信號(hào)(electroencephalogram, EEG)的空域特征。但是共空域模式算法的性能很大程度上也取決腦電信號(hào)的頻域信息。因此如何提取腦電信號(hào)的最優(yōu)頻帶就顯得尤為重要。為了有效地解決腦電信號(hào)頻帶優(yōu)化選擇的問題,本文提出了兩種基于CSP的頻帶優(yōu)化選擇算法。 第一種算法是小波包系數(shù)加權(quán)的方法,適用于二進(jìn)制(即二分類)腦-機(jī)接口。該算法在小波包分解對(duì)頻帶劃分的理論基礎(chǔ)上,通過對(duì)小波包系數(shù)加權(quán)來實(shí)現(xiàn)最優(yōu)頻帶的選擇；第二種方法適用于多分類腦-機(jī)接口,是濾波器組與特征選擇相結(jié)合的頻帶優(yōu)選方法。該算法使用濾波器組將原始的腦電信號(hào)分解為為多個(gè)子帶信號(hào),使用CSP算法提取每個(gè)子帶信號(hào)的特征,通過特征選擇實(shí)現(xiàn)頻帶優(yōu)化。在離線分析實(shí)驗(yàn)中,兩種算法都取得了不錯(cuò)的分類效果。相對(duì)于寬帶方法,這兩種算法都使分類識(shí)別率得到較大幅度的提升。
[Abstract]:Brain-Computer Interface (BCI) is a technology that converts brain consciousness signals into external output commands. In brain-computer interface systems based on motion imagination, Common Spatial pattern (CSP) is a successful algorithm, which has the advantage of designing an optimal spatial filter. The spatial feature of EEG can be extracted by spatial filtering. However, the performance of the common spatial pattern algorithm also depends on the frequency domain information of EEG to a great extent. Therefore, how to extract the optimal frequency band of EEG signal is obvious. In order to effectively solve the problem of optimal selection of EEG frequency band, In this paper, two optimal selection algorithms based on CSP are proposed. The first method is the weighted method of wavelet packet coefficient, which is suitable for binary (i.e. two-classification) brain-computer interface. This algorithm is based on the theory of wavelet packet decomposition of the frequency band partition. The selection of optimal frequency band is realized by weighted wavelet packet coefficient. The second method is suitable for multi-classification brain-computer interface. The algorithm uses the filter bank to decompose the original EEG signal into multiple sub-band signals, and uses the CSP algorithm to extract the features of each sub-band signal. In off-line analysis experiments, the two algorithms have achieved good classification results. Compared with the wideband method, both of these two algorithms can improve the classification recognition rate by a large margin.
【學(xué)位授予單位】：南昌大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP334.7

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本文編號(hào)：1605634