共查询到19条相似文献,搜索用时 515 毫秒
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针对实际采集的脑电信号受眼电干扰较大,提出一种基于离散小波变换(DWT)与独立分量分析(ICA)的自动去除眼电伪迹的方法(DWICA).对采集的多导脑电和眼电信号进行离散小波变换,获取多尺度小波系数,将串接小波系数作为ICA的输入;利用基于负熵判据的FastICA算法实现独立成分的快速获取,引入夹角余弦准则自动识别眼迹成分,并经过ICA逆变换将剔除眼迹后的独立成分投影返回到原脑电信号各个电极;通过DWT逆变换重构信号,即可得到去除眼迹的各导脑电信号.实验结果表明,DWICA方法极大地提高了脑电信号的信噪比,抗噪能力强且实时性好,为脑电信号的在线预处理提供了新思路. 相似文献
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脑电数据经常被各种电生理信号伪迹所污染。在常见伪迹中,肌电伪迹特别难以去除。文献中最常用的方法包括诸如独立分量分析(Independent Component Analysis, ICA)和典型相关分析(Canonical Correlation Analysis, CCA)等盲源分离技术。该文首次提出一种基于独立向量分析(Independent Vector Analysis, IVA)的新方法,用以去除脑电中的肌电伪迹。IVA同时使用高阶统计量和二阶统计量,因此该方法能够充分利用肌电伪迹的非高斯性和弱相关性,兼具ICA方法和CCA方法的优势。实验表明,使用IVA方法可以在保留脑电成份的同时极大抑制肌电伪迹,效果显著优于ICA法和CCA法。 相似文献
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脑电信号是一种复杂且重要的生物信号,被广泛应用于类脑智能技术和脑机接口领域的研究。文中介绍了干扰正常脑电信号的常见非生理性伪迹和生理性伪迹的类型及特点,并对生理性伪迹的产生原因进行了详细分析。通过对各种脑电信号去除伪迹方法的回顾以及应用现状的分析,比较并总结了传统去除伪迹方法和新型去除伪迹方法的研究进展,并进一步分析去除伪迹方法的优缺点。部分方法已经成功应用于处理脑电信号中的眼电、心电和肌电等伪迹中。文中还针对目前脑电信号去除伪迹的需求及所面临的问题给出了应对策略,并对未来的研究方向进行了分析和展望。 相似文献
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当前主流的眼电(EOG)去除方法需要利用多通道脑电的相关性,难以在单通道的便携式脑机接口(BCI)中应用。该文提出一种基于长时差分振幅包络与小波变换的眼电干扰自动分离方法。首先在原脑电信号的长时差分振幅包络上实施双门限法来精确检测眼电的起止点,然后利用sym5小波对脑电进行分解并引进Birg_Massart策略来自适应地确定小波重构系数阈值,最后通过小波重构精确地估计眼电,实现单通道上眼电与脑电的自动分离。大量实验证明,该方法与主流的平均伪迹回归分析和基于独立成分分析(ICA)的方法相比,能够获得更好的估计眼电与原眼电的相关性,保证更高的校正信噪比和较强的实时性,能够满足脑机接口多方面的需要。 相似文献
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在脑电信号的采集和处理过程中,经常受到如眼电、心电等各样噪声和伪迹的影响。独立分量分析通过对非高斯分布数据进行有效表示,获得在统计学上独立的各个分量,通过对噪声分量的去除以及信号分量的重构,实现对噪声和伪迹的去除。针对目前信号分解后噪声分量的处理尚停留在目测去除和人工识别阶段,耗时严重以及准确度差的不足,本文提出一种基于独立分量分析的KC复杂度自动阈值算法的提出很好地解决了这个问题,在对含工频噪声的EEG信号进行处理后,取得了良好的实验效果。 相似文献
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《现代电子技术》2021,(1):39-44
针对脑电(EEG)信号在采集过程中易受到肌电(EMG)伪迹干扰,且EMG伪迹复杂多变难以去除的问题,提出一种基于主分量分析(PCA)和自适应步长独立向量分析(IVA)相结合的EEG中EMG伪迹去除方法。首先,利用PCA将EEG信号的主分量提取出来,对数据降维;然后对主分量引入IVA算法,根据高阶统计量和二阶统计量,结合EMG伪迹的非高斯性和弱相关性进行EMG伪迹分离,同时引入基于分离效果的自适应步长选取方法,增强分离效果。实验中采集了8通道的EEG信号,测得各通道相对均方根误差为0.09~0.2,算法的平均EMG伪迹分离率为98%,且相比单独使用IVA时间节省20%,该方法适用于动态EEG中EMG伪迹的去除。 相似文献
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Correlation of signals at multiple scales of observation is useful for multiresolution interpretation of image, data and target signature analysis. Multiresolution analysis is inherent in the discrete wavelet transform (DWT), but shift-variance of the coefficients of the transform in dyadic orthogonal and biorthogonal basis spaces is the problem associated with it. Shift-variance of the transform and absence of a direct transform domain relationship make correlation of signals by the DWT inconvenient at multiple scales. The circulant shift property of the DWT coefficients is used in a novel way to produce correlation of signals at multiple scales with the critically sampled DWT only. The algorithm is derived in both discrete time and z-domain for signal vectors of finite duration. The algorithm is independent of signal waveform and wavelet kernel and is applied particularly for multiple scale correlation of radar signals, namely linear frequency modulated (LFM) chirp signals. 相似文献
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There are numerous neurological disorders such as dementia, headache, traumatic brain injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological disorder in the human after stroke. Electroencephalogram (EEG) contains valuable information related to different physiological state of the brain. A scheme is presented for detecting epileptic seizures from EEG data recorded from normal subjects and epileptic patients. The scheme is based on discrete wavelet transform (DWT) analysis and approximate entropy (ApEn) of EEG signals. Seizure detection is performed in two stages. In the first stage, EEG signals are decomposed by DWT to calculate approximation and detail coefficients. In the second stage, ApEn values of the approximation and detail coefficients are calculated. Significant differences have been found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with 100 % classification accuracy using artificial neural network. The analysis results depicted that during seizure activity, EEG had lower ApEn values compared to normal EEG. This gives that epileptic EEG is more predictable or less complex than the normal EEG. In this study, feed-forward back-propagation neural network has been used for classification and training algorithm for this network that updates the weight and bias values according to Levenberg–Marquardt optimization technique. 相似文献
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We perform a thorough data dependence and localization analysis for the discrete wavelet transform algorithm and then use it to synthesize distributed memory and control architectures for its parallel computation. The discrete wavelet transform (DWT) is characterized by a nonuniform data dependence structure owing to the decimation operation it is neither a uniform recurrence equation (URE) nor an affine recurrence equation (ARE) and consequently cannot be transformed directly using linear space-time mapping methods into efficient array architectures. Our approach is to apply first appropriate nonlinear transformations operating on the algorithm's index space, leading to a new DWT formulation on which application of linear space-time mapping can become effective. The first transformation of the algorithm achieves regularization of interoctave dependencies but alone does not lead to efficient array solutions after the mapping due to limitations associated with transforming the three-dimensional (3-D) algorithm onto one-dimensional (1-D) arrays, which is also known as multiprojection. The second transformation is introduced to remove the need for multiprojection by formulating the regularized DWT algorithm in a two-dimensional (2-D) index space. Using this DWT formulation, we have synthesized two VLSI-amenable linear arrays of LPEs computing a 6-octave DWT decomposition with latencies of M and 2M-1, respectively, where L is the wavelet filter length, and M is the number of samples in the data sequence. The arrays are modular, regular, use simple control, and can be easily extended to larger L and J. The latency of both arrays is independent of the highest octave J, and the efficiency is nearly 100% for any M with one design achieving the lowest possible latency of M 相似文献
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Aydin N. Marvasti F. Markus H.S. 《IEEE transactions on information technology in biomedicine》2004,8(2):182-190
Asymptomatic circulating emboli can be detected by Doppler ultrasound. Embolic Doppler ultrasound signals are short duration transient like signals. The wavelet transform is an ideal method for analysis and detection of such signals by optimizing time-frequency resolution. We propose a detection system based on the discrete wavelet transform (DWT) and study some parameters, which might be useful for describing embolic signals (ES). We used a fast DWT algorithm based on the Daubechies eighth-order wavelet filters with eight scales. In order to evaluate feasibility of the DWT of ES, two independent data sets, each comprising of short segments containing an ES (N=100), artifact (N=100) or Doppler speckle (DS) (N=100), were used. After applying the DWT to the data, several parameters were evaluated. The threshold values used for both data sets were optimized using the first data set. While the DWT coefficients resulting from artifacts dominantly appear at the higher scales (five, six, seven, and eight), the DWT coefficients at the lower scales (one, two, three, and four) are mainly dominated by ES and DS. The DWT is able to filter out most of the artifacts inherently during the transform process. For the first data set, 98 out of 100 ES were detected as ES. For the second data set, 95 out of 100 ES were detected as ES when the same threshold values were used. The algorithm was also tested with a third data set comprising 202 normal ES; 198 signals were detected as ES. 相似文献
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利用独立分量分析(ICA)和离散小波变换(DWT),提出了一种新的视频数字水印算法。该算法将二值图像和灰度图像分别置乱后用ICA混叠得到两幅图像,选取其中一个用来做嵌入的水印,另一个作为密钥保留,以此来增加水印的安全性;用ICA将水印嵌入在原始视频的DWT低频子带系数中,实现了水印信号的盲检测,同时针对帧剪裁、帧丢失、帧平均和MPEG编码等具有良好的鲁棒性。 相似文献
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The mu rhythm is an electroencephalogram (EEG) signal located at the central region of the brain that is frequently used for studies concerning motor activity. Quite often, the EEG data are contaminated with artifacts and the application of blind source separation (BSS) alone is insufficient to extract the mu rhythm component. We present a new two-stage approach to extract the mu rhythm component. The first stage uses second-order blind identification (SOBI) with stationary wavelet transform (SWT) to automatically remove the artifacts. In the second stage, SOBI is applied again to find the mu rhythm component. Our method is first compared with independent component analysis with discrete wavelet transform (ICA-DWT) as well as SOBI-DWT, ICA-SWT, and regression method for artifact removal using simulated EEG data. The results showed that the regression method is more effective in removing electrooculogram (EOG) artifacts, while SOBI-SWT is more effective in removing electromyogram (EMG) artifacts as compared to the other artifact removal methods. Then, all the methods are compared with the direct application of SOBI in extracting mu rhythm components on simulated and actual EEG data from ten subjects. The results showed that the proposed method of SOBI-SWT artifact removal enhances the extraction of the mu rhythm component. 相似文献