事件相关脑电信号单导少次提取与分类算法
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摘要
不同的刺激事件既可能在脑电信号中产生事件相关电位(Event-Related Potential, ERP),也可能以事件相关去同步／同步(Event-Related Desynchronization/Synchronization, ERD/ERS)的形式使脑电信号发生变化,所以ERP和ERD/ERS从两个不同方面反映了大脑内部的神经活动。ERP是锁时锁相的,表现为时域中特定的波形；而ERD/ERS是锁时非锁相的,体现在脑电信号频带能量的变化之中。本文主要研究ERP的单导联少次提取和基于ERD/ERS的运动想象脑电信号特征分类问题,不仅有助于丰富大脑科学研究的内容,也有助于人工智能技术的发展。主要创新工作包括：
(1)从空域角度提出了虚拟信号通道独立分量分析方法实现ERP的单导联少次提取。针对独立分量分析算法在ERP提取中的适用性问题,使用少次记录信号构建虚拟信号通道模型的观测信号矩阵,使独立分量分析算法能够应用于单导联观测信号处理中,实现了独立性准则下加权叠加平均效果。
(2)从时域角度提出了广义子空间矩阵滤波法实现ERP的单导联少次提取。针对传统滤波器去噪能力的局限性,使用矩阵滤波器对观测信号进行处理。根据广义子空间法原理,推出了滤波矩阵的结构可以表示为投影矩阵、系数加权矩阵与重构矩阵的连乘。利用两次记录信号计算投影矩阵,并在最小均方误差准则下得到系数加权矩阵,清晰地推导出了最优滤波矩阵。在广义子空间矩阵滤波法的基础上进一步提出了平均子空间矩阵滤波法,实现了更好的ERP提取效果。
(3)从变换域角度提出了加权阈值小波分析方法实现ERP的单导联单次提取。针对ERP的低信噪比特性,首先将观测信号经过白化滤波器进行滤波,使其中的自发脑电转化为白噪声,然后对滤波后信号的小波系数进行加权阈值处理,最后通过小波反变换与信号还原得到ERP的估计。加权阈值小波分析方法摆脱了对先验信息的依赖性,更加符合实际应用背景,取得了比传统阈值法更好的处理效果。
(4)针对基于ERD/ERS的运动想象脑电信号特征分类问题,提出了自适应小波基和自适应投影基的特征提取方法,增大不同类别间特征向量的差异。然后结合序贯似然比检验分类算法,实现了准确率与实时性的折中,提高了算法的灵活性和鲁棒性。
An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing electroencephalogram (EEG) in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The ERD/ERS is highly frequency band-specific. Therefore, it is import to study the single-channel few-trial ERP extraction and motor imagery EEG signals feature extraction and classification problems. It will not only help to enrich the contents of the human brain cognitive science and neuroscience, but contribute to the development of artificial intelligence technology. Aiming at these targets, the contribution of this thesis includes the following work.
(1) A virtual signal channel independent component analysis (ICA) method was proposed to realize ERP few-trial extraction from space domain viewpoint. After analyzing the applicability of ICA for the single channel ERP few-trial extraction problem, a virtual channel ICA model was presented to make it comply with the premise of the ICA method. With this new model, the record signals from single channel can be realized with only four-trials.
(2) A subspace matrix filtering approach was advanced to estimate single channel ERP with few trials from time domain viewpoint. Considering the limitation of the traditional convolution (vector) filter in noise reduction, a matrix filter for few-trial ERP extraction was given. Based on the principle of the generalized subspace approach (GSA), the filter matrix could be expressed as the multiplication of projection matrix, coefficient weighted matrix and reconstruction matrix. Then the projection matrix was obtained with the observed noisy signals and the coefficient weighted matrix was calculated under the minimum mean square error (MMSE) criterion. The ERP signal was then obtained by averaging the signals estimated with the reconstruction matrix. The algorithm can estimate the ERP signal with only two trials observable noisy signals which greatly reduced the number of trials required.
(3) A weighted-thresholding wavelet analysis was described to estimate ERP signals with single trial from transform domain viewpoint. The underlying principle was to filter the noisy signal and turn the EEG into white noise. Then, through wavelet transform, the coefficients of the noise were distributed in all scales and shifts while those of the expected ERP signal concentrated only in several scales. The expected signal was estimated by weighting the coefficients and inverse transformation. This algorithm is free from the dependence on the prior information of the EP signals, eliminating the choice of wavelet coefficients by experience which is in line with the application background. Moreover, it can extract ERP signals with only one trial record.
(4) For ERD/ERS in the motor imagery (MI) EEG. two adaptive feature extraction methods were given to extract the EEG features that reflect the different mind states. A sequential likelihood ratio test (SPRT) was combined to classify the features. The proposed technique not only improved the classification accuracy and transformation rate, but also and balanced the tradeoff between speed and accuracy. Experimental results suggested the possibility of improving the quality of BCI.
(1)从空域角度提出了虚拟信号通道独立分量分析方法实现ERP的单导联少次提取。针对独立分量分析算法在ERP提取中的适用性问题,使用少次记录信号构建虚拟信号通道模型的观测信号矩阵,使独立分量分析算法能够应用于单导联观测信号处理中,实现了独立性准则下加权叠加平均效果。
(2)从时域角度提出了广义子空间矩阵滤波法实现ERP的单导联少次提取。针对传统滤波器去噪能力的局限性,使用矩阵滤波器对观测信号进行处理。根据广义子空间法原理,推出了滤波矩阵的结构可以表示为投影矩阵、系数加权矩阵与重构矩阵的连乘。利用两次记录信号计算投影矩阵,并在最小均方误差准则下得到系数加权矩阵,清晰地推导出了最优滤波矩阵。在广义子空间矩阵滤波法的基础上进一步提出了平均子空间矩阵滤波法,实现了更好的ERP提取效果。
(3)从变换域角度提出了加权阈值小波分析方法实现ERP的单导联单次提取。针对ERP的低信噪比特性,首先将观测信号经过白化滤波器进行滤波,使其中的自发脑电转化为白噪声,然后对滤波后信号的小波系数进行加权阈值处理,最后通过小波反变换与信号还原得到ERP的估计。加权阈值小波分析方法摆脱了对先验信息的依赖性,更加符合实际应用背景,取得了比传统阈值法更好的处理效果。
(4)针对基于ERD/ERS的运动想象脑电信号特征分类问题,提出了自适应小波基和自适应投影基的特征提取方法,增大不同类别间特征向量的差异。然后结合序贯似然比检验分类算法,实现了准确率与实时性的折中,提高了算法的灵活性和鲁棒性。
An internally or externally paced event results not only in the generation of an event-related potential (ERP) but also in a change in the ongoing electroencephalogram (EEG) in form of an event-related desynchronization (ERD) or event-related synchronization (ERS). The ERP on the one side and the ERD/ERS on the other side are different responses of neuronal structures in the brain. While the former is phase-locked, the latter is not phase-locked to the event. The ERD/ERS is highly frequency band-specific. Therefore, it is import to study the single-channel few-trial ERP extraction and motor imagery EEG signals feature extraction and classification problems. It will not only help to enrich the contents of the human brain cognitive science and neuroscience, but contribute to the development of artificial intelligence technology. Aiming at these targets, the contribution of this thesis includes the following work.
(1) A virtual signal channel independent component analysis (ICA) method was proposed to realize ERP few-trial extraction from space domain viewpoint. After analyzing the applicability of ICA for the single channel ERP few-trial extraction problem, a virtual channel ICA model was presented to make it comply with the premise of the ICA method. With this new model, the record signals from single channel can be realized with only four-trials.
(2) A subspace matrix filtering approach was advanced to estimate single channel ERP with few trials from time domain viewpoint. Considering the limitation of the traditional convolution (vector) filter in noise reduction, a matrix filter for few-trial ERP extraction was given. Based on the principle of the generalized subspace approach (GSA), the filter matrix could be expressed as the multiplication of projection matrix, coefficient weighted matrix and reconstruction matrix. Then the projection matrix was obtained with the observed noisy signals and the coefficient weighted matrix was calculated under the minimum mean square error (MMSE) criterion. The ERP signal was then obtained by averaging the signals estimated with the reconstruction matrix. The algorithm can estimate the ERP signal with only two trials observable noisy signals which greatly reduced the number of trials required.
(3) A weighted-thresholding wavelet analysis was described to estimate ERP signals with single trial from transform domain viewpoint. The underlying principle was to filter the noisy signal and turn the EEG into white noise. Then, through wavelet transform, the coefficients of the noise were distributed in all scales and shifts while those of the expected ERP signal concentrated only in several scales. The expected signal was estimated by weighting the coefficients and inverse transformation. This algorithm is free from the dependence on the prior information of the EP signals, eliminating the choice of wavelet coefficients by experience which is in line with the application background. Moreover, it can extract ERP signals with only one trial record.
(4) For ERD/ERS in the motor imagery (MI) EEG. two adaptive feature extraction methods were given to extract the EEG features that reflect the different mind states. A sequential likelihood ratio test (SPRT) was combined to classify the features. The proposed technique not only improved the classification accuracy and transformation rate, but also and balanced the tradeoff between speed and accuracy. Experimental results suggested the possibility of improving the quality of BCI.
引文
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