基于视觉诱发电位的脑—机接口分析算法优化及实时控制系统构建
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摘要
近年来,随着脑神经科学、电生理学、计算机科学、信号处理技术和生物医学工程等领域的飞速发展以及医疗康复领域的需要,脑-机接口(Brain-Computer Interface, BCI)技术正迅速成为全球科学家的研究热点之一。BCI是在大脑和计算机或其他外部设备之间建立不依赖于常规大脑信息输出通路(外周神经和肌肉组织)的全新通信和控制技术。借此,人们可以直接通过大脑的思维活动来表达想法或操纵设备,而不需要任何言语或动作。这使得严重残疾患者与外界环境的交互通信成为了可能,将有效地帮助提高他们的生活质量,同时也为健康用户提供了一种不同于传统通信形式(如,听觉,视觉和触觉形式等)的全新通信交流方式。
BCI的实现可以通过多种不同的脑信号形式来完成,如,非植入式的脑电图(Electroencephalogram, EEG),脑磁图(Magnetoencephalogram, MEG),功能磁共振成像(Functional Magnetic Resonance Imaging, fMRI),近红外光谱(Near-Infrared Spectroscopy, NIRS)和植入式的脑皮层电图(Electrocorticogram, ECoG)。由于EEG技术最为安全实用,且成本相对较低,因此其在BCI的研究中被应用最为广泛。本文采用这种非植入式EEG技术,主要对基于视觉诱发电位的BCI分析算法优化和实时控制系统构建问题进行了研究。研究工作所取得的主要成果如下：
(1)针对稳态视觉诱发电位(Steady-State Visual Evoked Potential, SSVEP)的识别过程中的相关性参考信号优化问题,将多维信号分析的思想引入典型相关分析,提出了一种多维典型相关分析(Multiway Canonical Correlation Analysis, MCCA)。 MCCA通过对三阶张量EEG数据(导联×时间×试验)与二维正余弦信号之间的多维相关性分析寻找最优的相关性参考信号。优化得到的刺激频率参考信号不仅准确反应了EEG信号中的SSVEP频率成分,同时包含了被试间差异性和试验间共性等信息。利用SSVEP诱发实验中记录的EEG数据,证实了所提出的MCCA算法有效地提高了在较短时间窗内的SSVEP识别精度。
(2)提出了一种基于最小绝对收缩和选择算子(Least Absolute Shrinkage and Selection Operator, LASSO)回归的稀疏表征识别模型,并将其应用于解决SSVEP的识别问题。该算法通过执行EEG信号与所有刺激频率参考信号之间LASSO回归,从而形成一个稀疏表征问题,稀疏的回归权值向量反映了EEG信号中最主导的频率成分,即当前的目标刺激频率。这种稀疏表征算法有效地增强了SSVEP识别的鲁棒性,缩短了识别所需的时间,从而提高了基于SSVEP的BCI系统实时性。
(3)从不同分析角度出发提出了三种事件相关电位(Event-Related Potential,ERP)空间特征优化算法：基于FastICA的P300快速提取算法,用于在少次试验下通过多导联去噪以准确地提取P300模式；基于离散粒子群优化的P300分类最优导联选择算法,通过群智能优化搜索产生最高分类性能的EEG导联组合,用以确定各被试的P300分类最优导联；基于Fisher准则的正则化共空间模式算法,通过多导联空间优化提取单次试验下具有高可判别性的模式特征,以提高ERP分类精度。利用Hoffmann's P300数据集和实验所采集的人脸感知ERP数据集,证实了所提出的上述三种ERP空间特征优化算法的有效性。
(4)理论上导联数量越多时所能提供的ERP特征信息就越丰富,从而可能得到更好的ERP分类效果。然而,多导联多采样点的串联会导致特征向量维数过高而出现所谓的维数灾现象,这将严重影响所学习分类器的泛化能力,从而导致较差的分类效果。针对该问题,将广泛应用于压缩传感领域的稀疏(Sparsity)正则化技术成功引入线性判别分析,提出了一种基于l1-范数(稀疏)正则化的线性判别分析算法(Sparse Linear Discriminant Analysis, SPLDA),并用于解决小样本高维特征下的ERP分类问题。通过大量的实验数据集证实了SPLDA算法对ERP准确分类的有效性。
(5)针对ERP分类问题中使用传统向量化高维特征时所带来的维数灾问题,将一般线性判别分析扩展至多维判别分析,提出了一种空间-时间判别分析算法(Spatial-Temporal Discriminant Analysis, STDA)。STDA算法通过对EEG的空间维和时间维进行交替协同优化以学习两个投影矩阵从而使投影后的特征在目标类和非目标类之间的可判别性达到最大化。利用所学习的两个投影矩阵将构造的各空间-时间二维样本转化为新的维数显著降低的一维样本,有效地改善了协方差矩阵参数估计,并增强了在小训练样本集下所学习分类器的泛化能力。利用BCI竞赛-Ⅲ's P300数据集和实验所采集的ERP数据集,证实了所提出的STDA算法对减少基于ERP的BCI系统校验时间的有效性,其对提高系统实用性有着十分重要的意义。
(6)将认知神经科学领域中广泛研究的人脸感知结构处理(Configural Processing)特性成功引入BCI系统的设计中,开发了基于人脸结构感知的多ERP融合BCI系统。该BCI系统的刺激范式采用了具有结构损失人脸作为刺激,并结合传统基于P300的BCI系统中的Oddball范式进行刺激呈现。生动形象的人脸刺激有助于帮助缓解被试长时间使用该系统后所可能产生的疲劳和反感,且人脸结构损失促使被试更加积极地注视刺激,并引起大脑中与结构信息处理相关的更高水平认知功能响应,即诱发出了显著的人脸结构处理事件相关VPP和N170成分。经过大量的刺激物实验比较分析,证实了基于人脸结构感知的多ERP(VPP、N170和P300)融合BCI系统的优良性能。
(7)在Simulink/Matlab (Mathworks Inc., USA)平台上成功开发了三种基于人脸结构感知相关的多ERP融合BCI控制系统：智能轮椅控制系统、机器假肢控制系统和机器人交互系统。这些系统均不要求用户有任何肌肉神经的动作,只需专注于所设计的BCI范式,即可实现对轮椅的驾驶操作、对机器假肢的移动操作(如抓取食物和传递物品等)以及针对机器人控制的多种操作。
In recent years, with rapid development of the neuroscience, electrophysiology, computer science, signal processing technology, biomedical engineering fields and requirement of the medical rehabilitation field, brain-computer interface (BCI) is becoming one of the hottest research topics of scientists around the world. BCI is a novel communication and control technique that allows direction connection between a human brain and a computer or other external device without depending on conventional information output paths (such as, peripheral nerve and muscle tissue) of the brain. With the BCI, people can express ideas and operate devices directly through thought activities in their brains without any speech and gesture. The BCI makes interaction communication between severely disabled subjects and external environment possible, and will improve effectively quality of their life. Also, the BCI provides an alternative means of communication to those traditional ones (such as, auditory, visual and tactile ways) for healthy subjects.
BCI can be developed using various means of brain signals, such as, non-invasive Electroencephalogram (EEG), Magnetoencephalogram (MEG), Functional Magnetic Resonance Imaging (fMRI), Near-Infrared Spectroscopy (NIRS), and invasive Electrocorticogram (ECoG). Since the EEG-based BCI is more secure with high practicability, and requires relatively inexpensive equipments, it has been more widely studied and developed. This study adopts the non-invasive EEG technique and focuses on analysis algorithm optimization and real-time system establishment for the visual evoked potential-based BCI. The main results achieved by this study are summarized as follows:
(1) Conception of multiway signal processing is introduced into canonical correlation analysis to solve the correlation reference signal optimization for SSVEP recognition. A multiway canonical correlation analysis (MCCA) is proposed. The MCCA algorithm implements correlation analysis between a three-order EEG tensor (channel X time X trial) and a two-way sine-cosine signal matrix, to find the optimal correlation reference signals. Optimized correlation reference signals contain important information of subject-specific variability and trial-to-trial consistency. Experimental results validate that such optimal correlation reference signals derived by the MCCA assist to improve the SSVEP recognition performance.
(2) A sparse representation recognition model based on least absolute shrinkage and selection operator (LASSO) regression is proposed for SSVEP recognition. The LASSO recognition model implements regression between the EEG and composite reference signals to form a sparse representation. The solved sparse weight vector implies the dominant frequency components in EEG and hence the current target stimulus frequency. Analysis results indicate the proposed LASSO recognition model enhances robustness and decreases required time for the SSVEP recognition, and hence improves practicability of the SSVEP-based BCI.
(3) Three spatial features optimization algorithms are proposed:P300fast extraction algorithm based on FastICA, which is to extract P300effectively using few trials EEG with muli-channel denoising; Channel selection algorithm based on discrete particle swarm optimization (DPSO) for P300classification, which is to search the optimal channel configuration yielding the best classification performance by swarm optimization; Fisher's criterion regularizd common spatial pattern (FCCSP), which is to extract the most discriminantive event-related potential (ERP) features from single-trial by multi-channel spatial optimization, and hence to improve classification performance. The aforementioned three algorithms are validated based on the Hoffmann's P300dataset and face perception ERP dataset, respectively.
(4) Theoretically, more channels provide richer information of ERP features, and hence should yield better ERP classification performance. However, the feature vectors formed by concatenation of multi-point from muli-channel are typically high dimensional, and most probably result in the so-called curse-of-dimensionality that will depress seriously generalization capacity of the trained classifier and cause poor classification performance. To solve such problem, a sparsity regularization technique, which has been widely discussed in compressed sensing, is introduced into linear discriminant analysis to propose a l1-norm (sparsity) regularized linear discriminant analysis (SPLDA). Extensive experimental tests validate the SPLDA can classifify ERP effectively even with few high-dimensional features.
(5) A spatial-temporal discriminant analysis (STDA) is proposed based on multiway extension of the traditional LDA to solve the curse-of-dimensionality caused by the vectorized high-dimensional features in the ERP classification. The STDA implements collaboratively optimization in the spatial and temporal dimensions of EEG to learn two projection matrices rendering the projected features have maximal discriminantive information between target and non-target classes. The two learned projection matrices are then used to transform the constructed spatial-temporal two-way samples to new one-way samples with much lower dimensionality, which improves significantly covariance matrices estimation in the subsequent discriminant analysis, and hence enhances generalization capacity of the learned classifier. The proposed STDA is validated with the BCI Competition-Ⅲ's dataset and our own experimental dataset. Results show the STDA is effective to reduce system calibration time of the ERP-based BCI, which is considerably important to improve the practicability of BCI system.
(6) Configural information processing of human faces, which has been widely researched in the field of cognitive neuroscience, is successfully introduced into the BCI design, such that a hybrid BCI system exploiting simutaneously multiple ERPs is developed. The proposed BCI system is based on an oddball paradigm using stimuli of facial images with loss of configural face imformation. The vivid facial images are ideally effective to resist fatigue and discomfort of subjects for long time use. Loss of configural information makes face perception more difficult and associated with higher-level cognitive functions, which encourages subjects to focus attension on the target more actively, and elicits significantly discriminative ERPs (VPP and N170). Extensive investigations on various types of stimuli validate effectiveness of the proposed hybrid BCI system exploiting simutaneously multiple ERPs (VPP, N170and P300).
(7) With the Simulink/Matlab (Mathworks Inc., USA) plantform, three real-time BCI control systems are developed using the multiple ERPs-based hybrid BCI paradigm: Intelligent wheelchair control system; robot arm control system; humanoid robots interaction system. All the three systems require not any neuromuscular function but only attention function of brain to realize wheelchair navigation, robot arm operation (e.g., catch food and deliver goods), and humanoid robots control.
BCI的实现可以通过多种不同的脑信号形式来完成,如,非植入式的脑电图(Electroencephalogram, EEG),脑磁图(Magnetoencephalogram, MEG),功能磁共振成像(Functional Magnetic Resonance Imaging, fMRI),近红外光谱(Near-Infrared Spectroscopy, NIRS)和植入式的脑皮层电图(Electrocorticogram, ECoG)。由于EEG技术最为安全实用,且成本相对较低,因此其在BCI的研究中被应用最为广泛。本文采用这种非植入式EEG技术,主要对基于视觉诱发电位的BCI分析算法优化和实时控制系统构建问题进行了研究。研究工作所取得的主要成果如下：
(1)针对稳态视觉诱发电位(Steady-State Visual Evoked Potential, SSVEP)的识别过程中的相关性参考信号优化问题,将多维信号分析的思想引入典型相关分析,提出了一种多维典型相关分析(Multiway Canonical Correlation Analysis, MCCA)。 MCCA通过对三阶张量EEG数据(导联×时间×试验)与二维正余弦信号之间的多维相关性分析寻找最优的相关性参考信号。优化得到的刺激频率参考信号不仅准确反应了EEG信号中的SSVEP频率成分,同时包含了被试间差异性和试验间共性等信息。利用SSVEP诱发实验中记录的EEG数据,证实了所提出的MCCA算法有效地提高了在较短时间窗内的SSVEP识别精度。
(2)提出了一种基于最小绝对收缩和选择算子(Least Absolute Shrinkage and Selection Operator, LASSO)回归的稀疏表征识别模型,并将其应用于解决SSVEP的识别问题。该算法通过执行EEG信号与所有刺激频率参考信号之间LASSO回归,从而形成一个稀疏表征问题,稀疏的回归权值向量反映了EEG信号中最主导的频率成分,即当前的目标刺激频率。这种稀疏表征算法有效地增强了SSVEP识别的鲁棒性,缩短了识别所需的时间,从而提高了基于SSVEP的BCI系统实时性。
(3)从不同分析角度出发提出了三种事件相关电位(Event-Related Potential,ERP)空间特征优化算法：基于FastICA的P300快速提取算法,用于在少次试验下通过多导联去噪以准确地提取P300模式；基于离散粒子群优化的P300分类最优导联选择算法,通过群智能优化搜索产生最高分类性能的EEG导联组合,用以确定各被试的P300分类最优导联；基于Fisher准则的正则化共空间模式算法,通过多导联空间优化提取单次试验下具有高可判别性的模式特征,以提高ERP分类精度。利用Hoffmann's P300数据集和实验所采集的人脸感知ERP数据集,证实了所提出的上述三种ERP空间特征优化算法的有效性。
(4)理论上导联数量越多时所能提供的ERP特征信息就越丰富,从而可能得到更好的ERP分类效果。然而,多导联多采样点的串联会导致特征向量维数过高而出现所谓的维数灾现象,这将严重影响所学习分类器的泛化能力,从而导致较差的分类效果。针对该问题,将广泛应用于压缩传感领域的稀疏(Sparsity)正则化技术成功引入线性判别分析,提出了一种基于l1-范数(稀疏)正则化的线性判别分析算法(Sparse Linear Discriminant Analysis, SPLDA),并用于解决小样本高维特征下的ERP分类问题。通过大量的实验数据集证实了SPLDA算法对ERP准确分类的有效性。
(5)针对ERP分类问题中使用传统向量化高维特征时所带来的维数灾问题,将一般线性判别分析扩展至多维判别分析,提出了一种空间-时间判别分析算法(Spatial-Temporal Discriminant Analysis, STDA)。STDA算法通过对EEG的空间维和时间维进行交替协同优化以学习两个投影矩阵从而使投影后的特征在目标类和非目标类之间的可判别性达到最大化。利用所学习的两个投影矩阵将构造的各空间-时间二维样本转化为新的维数显著降低的一维样本,有效地改善了协方差矩阵参数估计,并增强了在小训练样本集下所学习分类器的泛化能力。利用BCI竞赛-Ⅲ's P300数据集和实验所采集的ERP数据集,证实了所提出的STDA算法对减少基于ERP的BCI系统校验时间的有效性,其对提高系统实用性有着十分重要的意义。
(6)将认知神经科学领域中广泛研究的人脸感知结构处理(Configural Processing)特性成功引入BCI系统的设计中,开发了基于人脸结构感知的多ERP融合BCI系统。该BCI系统的刺激范式采用了具有结构损失人脸作为刺激,并结合传统基于P300的BCI系统中的Oddball范式进行刺激呈现。生动形象的人脸刺激有助于帮助缓解被试长时间使用该系统后所可能产生的疲劳和反感,且人脸结构损失促使被试更加积极地注视刺激,并引起大脑中与结构信息处理相关的更高水平认知功能响应,即诱发出了显著的人脸结构处理事件相关VPP和N170成分。经过大量的刺激物实验比较分析,证实了基于人脸结构感知的多ERP(VPP、N170和P300)融合BCI系统的优良性能。
(7)在Simulink/Matlab (Mathworks Inc., USA)平台上成功开发了三种基于人脸结构感知相关的多ERP融合BCI控制系统：智能轮椅控制系统、机器假肢控制系统和机器人交互系统。这些系统均不要求用户有任何肌肉神经的动作,只需专注于所设计的BCI范式,即可实现对轮椅的驾驶操作、对机器假肢的移动操作(如抓取食物和传递物品等)以及针对机器人控制的多种操作。
In recent years, with rapid development of the neuroscience, electrophysiology, computer science, signal processing technology, biomedical engineering fields and requirement of the medical rehabilitation field, brain-computer interface (BCI) is becoming one of the hottest research topics of scientists around the world. BCI is a novel communication and control technique that allows direction connection between a human brain and a computer or other external device without depending on conventional information output paths (such as, peripheral nerve and muscle tissue) of the brain. With the BCI, people can express ideas and operate devices directly through thought activities in their brains without any speech and gesture. The BCI makes interaction communication between severely disabled subjects and external environment possible, and will improve effectively quality of their life. Also, the BCI provides an alternative means of communication to those traditional ones (such as, auditory, visual and tactile ways) for healthy subjects.
BCI can be developed using various means of brain signals, such as, non-invasive Electroencephalogram (EEG), Magnetoencephalogram (MEG), Functional Magnetic Resonance Imaging (fMRI), Near-Infrared Spectroscopy (NIRS), and invasive Electrocorticogram (ECoG). Since the EEG-based BCI is more secure with high practicability, and requires relatively inexpensive equipments, it has been more widely studied and developed. This study adopts the non-invasive EEG technique and focuses on analysis algorithm optimization and real-time system establishment for the visual evoked potential-based BCI. The main results achieved by this study are summarized as follows:
(1) Conception of multiway signal processing is introduced into canonical correlation analysis to solve the correlation reference signal optimization for SSVEP recognition. A multiway canonical correlation analysis (MCCA) is proposed. The MCCA algorithm implements correlation analysis between a three-order EEG tensor (channel X time X trial) and a two-way sine-cosine signal matrix, to find the optimal correlation reference signals. Optimized correlation reference signals contain important information of subject-specific variability and trial-to-trial consistency. Experimental results validate that such optimal correlation reference signals derived by the MCCA assist to improve the SSVEP recognition performance.
(2) A sparse representation recognition model based on least absolute shrinkage and selection operator (LASSO) regression is proposed for SSVEP recognition. The LASSO recognition model implements regression between the EEG and composite reference signals to form a sparse representation. The solved sparse weight vector implies the dominant frequency components in EEG and hence the current target stimulus frequency. Analysis results indicate the proposed LASSO recognition model enhances robustness and decreases required time for the SSVEP recognition, and hence improves practicability of the SSVEP-based BCI.
(3) Three spatial features optimization algorithms are proposed:P300fast extraction algorithm based on FastICA, which is to extract P300effectively using few trials EEG with muli-channel denoising; Channel selection algorithm based on discrete particle swarm optimization (DPSO) for P300classification, which is to search the optimal channel configuration yielding the best classification performance by swarm optimization; Fisher's criterion regularizd common spatial pattern (FCCSP), which is to extract the most discriminantive event-related potential (ERP) features from single-trial by multi-channel spatial optimization, and hence to improve classification performance. The aforementioned three algorithms are validated based on the Hoffmann's P300dataset and face perception ERP dataset, respectively.
(4) Theoretically, more channels provide richer information of ERP features, and hence should yield better ERP classification performance. However, the feature vectors formed by concatenation of multi-point from muli-channel are typically high dimensional, and most probably result in the so-called curse-of-dimensionality that will depress seriously generalization capacity of the trained classifier and cause poor classification performance. To solve such problem, a sparsity regularization technique, which has been widely discussed in compressed sensing, is introduced into linear discriminant analysis to propose a l1-norm (sparsity) regularized linear discriminant analysis (SPLDA). Extensive experimental tests validate the SPLDA can classifify ERP effectively even with few high-dimensional features.
(5) A spatial-temporal discriminant analysis (STDA) is proposed based on multiway extension of the traditional LDA to solve the curse-of-dimensionality caused by the vectorized high-dimensional features in the ERP classification. The STDA implements collaboratively optimization in the spatial and temporal dimensions of EEG to learn two projection matrices rendering the projected features have maximal discriminantive information between target and non-target classes. The two learned projection matrices are then used to transform the constructed spatial-temporal two-way samples to new one-way samples with much lower dimensionality, which improves significantly covariance matrices estimation in the subsequent discriminant analysis, and hence enhances generalization capacity of the learned classifier. The proposed STDA is validated with the BCI Competition-Ⅲ's dataset and our own experimental dataset. Results show the STDA is effective to reduce system calibration time of the ERP-based BCI, which is considerably important to improve the practicability of BCI system.
(6) Configural information processing of human faces, which has been widely researched in the field of cognitive neuroscience, is successfully introduced into the BCI design, such that a hybrid BCI system exploiting simutaneously multiple ERPs is developed. The proposed BCI system is based on an oddball paradigm using stimuli of facial images with loss of configural face imformation. The vivid facial images are ideally effective to resist fatigue and discomfort of subjects for long time use. Loss of configural information makes face perception more difficult and associated with higher-level cognitive functions, which encourages subjects to focus attension on the target more actively, and elicits significantly discriminative ERPs (VPP and N170). Extensive investigations on various types of stimuli validate effectiveness of the proposed hybrid BCI system exploiting simutaneously multiple ERPs (VPP, N170and P300).
(7) With the Simulink/Matlab (Mathworks Inc., USA) plantform, three real-time BCI control systems are developed using the multiple ERPs-based hybrid BCI paradigm: Intelligent wheelchair control system; robot arm control system; humanoid robots interaction system. All the three systems require not any neuromuscular function but only attention function of brain to realize wheelchair navigation, robot arm operation (e.g., catch food and deliver goods), and humanoid robots control.
引文
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