本文選題：腦-機(jī)接口 + 獨(dú)立分量分析��； 參考：《生物醫(yī)學(xué)工程學(xué)雜志》2016年02期

【摘要】：在基于頭皮腦電(EEG)信號(hào)的腦-機(jī)接口(BCI)研究中,用戶個(gè)體差異性和背景噪聲的復(fù)雜性是影響B(tài)CI系統(tǒng)穩(wěn)定性的兩個(gè)主要因素。因此需要針對(duì)不同個(gè)體進(jìn)行BCI系統(tǒng)參數(shù)優(yōu)化,其中包括對(duì)時(shí)域、空域?yàn)V波器參數(shù)的優(yōu)化設(shè)計(jì)和分類器參數(shù)的學(xué)習(xí)。本文以提高BCI系統(tǒng)的準(zhǔn)確性為目標(biāo),提出了一種結(jié)合獨(dú)立分量分析空域?yàn)V波器(ICA-SF)優(yōu)化設(shè)計(jì)和EEG多子帶特征的BCI信息處理新方法�；谒岱椒�,對(duì)4位受試者在不同時(shí)間采集的三類運(yùn)動(dòng)想象EEG(MI-EEG)進(jìn)行分析。實(shí)驗(yàn)結(jié)果表明,在同一受試者的自交叉測(cè)試和不同受試者數(shù)據(jù)集之間的互交叉驗(yàn)證中,多子帶特征結(jié)合方法所得到的平均識(shí)別率比僅使用單頻帶所得的平均識(shí)別率普遍提高,識(shí)別率最大提升可達(dá)6.08%和5.15%。
[Abstract]:In the research of brain-computer interface (BCI) based on scalp EEG signal, the difference of user and the complexity of background noise are two main factors that affect the stability of BCI system. Therefore, it is necessary to optimize the parameters of BCI system for different individuals, including the optimization design of the parameters of time-domain and spatial filters and the learning of classifier parameters. In order to improve the accuracy of BCI system, this paper presents a new BCI information processing method which combines the optimization design of independent component analysis (ICA) spatial domain filter (ICA-SF) and the multi-subband feature of EEG. Based on the proposed method, three kinds of motion imagination (EEGMI-EEGG) collected by four subjects at different time were analyzed. The experimental results show that the average recognition rate obtained by the multi-subband feature combination method is generally higher than the average recognition rate obtained by using only one frequency band in the self-crossover test of the same subject and the cross-validation between different data sets. The recognition rate was increased by 6.08% and 5.15% respectively.
【作者單位】： 安徽大學(xué)計(jì)算智能與信號(hào)處理教育部重點(diǎn)實(shí)驗(yàn)室;安徽大學(xué)信息保障技術(shù)協(xié)同創(chuàng)新中心;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61271352;61401002)
【分類號(hào)】：R338;TN911.7

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1 戴若夢(mèng);基于深度學(xué)習(xí)的運(yùn)動(dòng)想象腦電分類[D];北京理工大學(xué);2015年

2 翟紅利;基于運(yùn)動(dòng)想象的腦機(jī)接口的數(shù)學(xué)模型與算法研究[D];長(zhǎng)沙理工大學(xué);2014年

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本文編號(hào)：2025590