参数模型法提取单次诱发脑电

本文提出一种诱发脑电的参数模型处理方法,充分利用刺激前脑电(EEG)信号及迭加平均诱发电位信号,可得到单次诱发电位的无偏、最小方差估计,提高信噪比近15dB。用计算机仿真检验了模型的性能。用本文所提方法处理实际的听觉诱发脑电,得到了诱发脑电幅度与刺激强度的正相关结果,证实了在实际应用中的可靠性。

结合小波变换和独立分量分析的脑电特征提取

在多道脑电信号的独立分量分析 ICA (Independent Com ponent Analysis)过程中 ,脑电独立源在数量上的不确定性往往是造成分离结果不理想的主要原因。目前还没有找到解决此问题的有效方法。根据脑电信号自身的特点 ,提出了一种结合小波变换 WT(Wavelet Transform)和独立分量分析的脑电特征提取新方法— WICA。新方法的主要思路是先对每一道脑电数据进行小波分解 ,然后根据需要选择不同尺度的小波子带信号进行脑电独立分量提取。实验结果表明 ,WICA方法在一定程度上能够克服因脑电独立源的不确定性所带来的影响 ,并能获得较好的脑电特征提取效果。

基于EMD-多尺度熵和ELM的运动想象脑电特征提取和模式识别

运动想象脑电特征是进行动作模式识别进而实现生物反馈技术的重要依据。在对侧躯体运动想象脑电识别方法的基础上,研究单侧躯体不同运动想象模式下的脑电特征提取问题,提出基于EMD-多尺度熵(MSE)的脑电信号瞬态特征提取及定量描述的方法,设计基于极限学习机(ELM)的动作模式识别模型。通过对10名正常受试者在左侧手臂屈、伸动作模式下的运动想象脑电的分析,提取其特征并进行动作识别,结果证实其识别率可以达到90%以上。实验表明:所提出基于EMD-MSE的运动想象EEG特征提取方法,能够定量刻画不同运动模式下脑电信号的多尺度局部瞬态特征;进一步运用基于ELM学习算法的前馈神经网络,可以实现对不同运动模式下脑电EMD-SME特征的有效分类。

基于小波包分解的时变脑电节律提取

研究从时变非平稳脑电信号中提取脑电动态节律的新方法。首先用小波包分解构造不同频率特性的时变滤波器以提取各种时变的脑电节律 ,研究临床脑电信号瞬时变化。在此基础上测试并分析两种不同功能状态下的脑电信号 ,并由此构造各种节律的时变脑电地形图。实验结果表明 ,小波包分解可以有效提取脑电不同节律的动态特性 ,此方法也适用于分析其他生物医学信号。

脑电波信号提取的研究

尽管当代科技飞速发展,但脑电技术在现今还有许多开发空间,脑电穿戴技术能够提供分辨大脑活动状态的能力。当人脑处于不同的状态下,大脑的主导脑电波便会产生变化。借助单片机的优秀性能,将其结合高敏感度的金属探头与人脑皮肤接触,从而可以实现提取大脑皮层电信号的能力。本文针对脑电提取设备的选用以及大脑皮层电信号的采集过程进行讲述。

运用独立分量分析提取兔体感诱发电位

目的采用独立分量分析提取兔体感诱发电位。方法麻醉兔,以0.5Hz频率刺激兔下肢隐神经,3764Hz采样率收集兔头皮电位。建立连续刺激同一采集点,采集3次混合背景噪声的信号,构建独立分量分析模型,用独立分量分析技术分解信号。结果获得了独立的体感诱发电位成分。结论连续延迟的信号源模型适合脑诱发电位,独立分量分析技术可以提取出诱发电位成分。

Noise-assisted MEMD based relevant IMFs identification and EEG classification

Noise-assisted multivariate empirical mode decomposition(NA-MEMD) is suitable to analyze multichannel electroencephalography(EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provide a highly localized time-frequency representation.For a finite set of multivariate intrinsic mode functions(IMFs) decomposed by NA-MEMD,it still raises the question on how to identify IMFs that contain the information of inertest in an efficient way,and conventional approaches address it by use of prior knowledge.In this work,a novel identification method of relevant IMFs without prior information was proposed based on NA-MEMD and Jensen-Shannon distance(JSD) measure.A criterion of effective factor based on JSD was applied to select significant IMF scales.At each decomposition scale,three kinds of JSDs associated with the effective factor were evaluated:between IMF components from data and themselves,between IMF components from noise and themselves,and between IMF components from data and noise.The efficacy of the proposed method has been demonstrated by both computer simulations and motor imagery EEG data from BCI competition IV datasets.

EEG Signal Denoising and Feature Extraction Using Wavelet Transform in Brain Computer Interface

Electroencephalogram(EEG) signal preprocessing is one of the most important techniques in brain computer interface(BCI).The target is to increase signal-to-noise ratio and make it more favorable for feature extraction and pattern recognition.Wavelet transform is a method of multi-resolution time-frequency analysis,it can decompose the mixed signals which consist of different frequencies into different frequency band.EEG signal is analyzed and denoised using wavelet transform.Moreover,wavelet transform can be used for EEG feature extraction.The energies of specific sub-bands and corresponding decomposition coefficients which have maximal separability according to the Fisher distance criterion are selected as features.The eigenvector for classification is obtained by combining the effective features from different channels.The performance is evaluated by separability and pattern recognition accuracy using the data set of BCI 2003 Competition,the final classification results have proved the effectiveness of this technology for EEG denoising and feature extraction.

An efficient approach of EEG feature extraction and classification for brain computer interface

In the study of brain-computer interfaces,a method of feature extraction and classification used fortwo kinds of imaginations is proposed.It considers Euclidean distance between mean traces recorded fromthe channels with two kinds of imaginations as a feature,and determines imagination classes using thresh-old value.It analyzed the background of experiment and theoretical foundation referring to the data sets ofBCI 2003,and compared the classification precision with the best result of the competition.The resultshows that the method has a high precision and is advantageous for being applied to practical systems.

An ERP Study in English Relative Clause Processing by Chinese-English Bilinguals

The processing of relative clauses receives much concern from linguists. The finding that object relatives are easier to process than subiect relatives in Chinese challenges the notion that subject relative clauses are preferred universally. A large body of literature provides theories related to sentence processing mechanisms for native speakers but leaves one area relatively untouched： how bilinguals process sentences. This study is designed to examine the case where the individuals with a Chinese L1 language background process subject-extracted subject relative clauses （SS） and subject-extracted object relative clauses （SO） by using event- related potentials （ERPs） to probe into the real-time language processing and presents a direct manifestation of brain activity. The findings from this study support the subject relative clause preference due to the strong influence of English relative clause markedness and bilinguals＇ relative lower working memory capacity.