运动想象脑电信号处理与P300刺激范式研究
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摘要
脑机接口(BCI)不依赖于外围神经和肌肉组织,在大脑和外围设备之间建立一条独立的信息传输通道,是一种新兴的人机交互方式。BCI技术在助残、康复、辅助控制、神经机器人、娱乐等领域有着广泛的应用前景。
在非植入式BCI研究中,基于头皮脑电(EEG)的采集方式具有无损伤、使用方便、设备便宜等优点,使得基于EEG的BCI技术具有很好的研究价值。用于BCI研究的EEG信号有多种模式,本文在充分研究各种模式特点的基础上,以运动想象脑电和P300电位为切入点,系统的研究了BCI系统构建、离线算法研究到在线实现的多个层面的问题,取得了较好的成果。
BCI的研究需要一个系统的软硬件平台,为了快速构建一套BCI系统,论文对目前的软硬件平台进行了研究。经过比较和已有实验条件,利用Neuroscan脑电采集设备和BCI2000软件系统快速构建了一套在线BCI系统。通过对BC12000的二次开发,积累了一些在线实验的经验,为实验室后续研究创造了条件。
信号处理算法将采集到的信号进行分析处理,识别出大脑意图并转换成控制指令,信号处理算法的好坏直接影响BCI系统性能。论文对BCI中的信号处理算法进行了研究,详细介绍了几种常用算法的原理和特点。针对BCI系统中存在的信息传输率较慢和脑电信号识别正确率较低的问题,对多通道四类运动想象脑电信号进行了研究,提出了一种基于共空间模式(CSP), Hilbert变换和支持向量机(SVM)的特征提取和分类算法。经过BCI竞赛数据的验证,该算法具有分类正确率高和时间开销小的特点,随着阈值的增大,平均分类正确率从87.22%提高到92.22%,但是时间开销增长到2.15倍,通过阈值的调节,在正确率和算法复杂度之间获得平衡。算法复杂度明显比采用多个SVM组合的多类分类算法要低,为实现算法的在线应用打下基础。
传统对脑电信号的识别通常是用不同的信号处理算法,然后选择一个最好的解决方案。然而,不同的信号处理算法性能存在差异,相互之间存在互补信息。为了充分利用各个信号处理算法的互补信息,论文提出了一种基于置信系数的组合分类算法,对不同分类难易程度的样本采用不同的分类策略。样本越容易分类,则组合分类算法侧重于提高算法的执行速度；样本越难分类,则组合分类算法侧重于提高算法的分类正确率。在提高算法分类能力同时兼顾算法的时间开销。经过BCI竞赛数据的验证,提出的基于置信系数的组合分类算法比分量分类器的平均分类能力提高15.4%。
基于P300的BCI是一类重要的BCI实现形式。自从Farwell和Donchin提出行列刺激范式(RCP)的P300字符输入系统以来,基于P300的BCI得到了广泛关注,研究人员从信号处理、刺激装置和实验范式等多个角度进行了广泛研究。论文则从实验范式角度进行了深入研究,提出了一种基于子矩阵的刺激范式(SBP),将整个字符矩阵划分为多个子矩阵,随机闪烁子矩阵中的一个字符,经过多次叠加,不仅可以提高脑电信号的信噪比,还能识别目标字符所在的子矩阵,显著提高了字符识别正确率和信息传输率。与RCP相比,SBP的最高实际信息传输率提升幅度达到10.8%。此外,SBP还具有不存在邻接干扰和双闪问题、子矩阵划分方式灵活、增加可选择的字符数量并不明显延长目标字符识别时间、更容易被受试者接受等优点。
为了进一步提高SBP的信息传输率并实现BCI的在线实验,论文提出了一种自适应的在线BCI系统。通过设定一个阈值来实现对P300叠加次数的动态调节,因此,阈值算法的研究是自适应算法的重要部分。论文在分析SBP原理和P300成分分布特点基础上,提出了最大值法和类峭度法两种阈值算法,均获得了较好的实验结果,其中最大值法可以获得最高34.36bits/min的实际信息传输率,比棋盘格刺激范式(CBP)的实际信息传输率提高9.04%,比Jin等人的自适应系统(NFA)的实际信息传输率提高11.2%。
Brain computer interface (BCI) builds an independent communication channel between the brain and external devices without using brain's normal output pathways of peripheral nerves and muscles, and acts as a new interactive way. BCI technology has a widely application prospect in rehabilitation, auxiliary control, neurorobotics, entertainment and so on.
In non-invasive BCI research, electroencephalography (EEG)-based BCIs are the most common for studies, because the recording method is safe, convenient and inexpensive. Various types of EEG signals have been used in BCIs. This thesis takes motor imagery EEG data and P300potential as key point, systematically describes the framework of the BCI, offline algorithm and online realization, and achieves some good results.
BCI is a complex system integrating hardware and software platforms. Now, several hardware and general-purpose software are available to build an online system rapidly. On the base of platform comparison and existing experimental condition, Neuroscan hardware devices and BCI2000software are combined to build an online BCI system. The thesis provides some online experimental experiences for future research on BCI by the secondary development of BCI2000.
Signal processing algorithm converts brain signals into control signals, the results of algorithm directly affect the performance of BCI system. The thesis presents a study on some frequently used signal processing method in detail. Due to the low information transfer rate and low recognition accuracy in BCI, the thesis proposes an algorithm based on common spatial pattern (CSP), Hilbert transformation and support vector machine (SVM) for feature extraction and classification of multi-channel four-class motor imagery EEG signals. With the datasets of BCI competition III, experimental results show that the proposed algorithm produces high classification accuracy and less time consumption, moreover, classification result can be further improved at the expense of algorithmic complexity by the threshold adjustment. With the increase of threshold, average classification accuracy is improved from87.22%to92.22%while the time consumption is increased to2.15times.
To choose the best solution, different feature extraction and classification algorithms are usually considered in BCI experiment. However, there is complementary information between different methodologies or different features that can improve the classification performance. In order to use the complementary information efficiently, the confidence coefficient is proposed to determine classification difficulty levels of samples in a classifier, and then combination of multiple classifiers based on confidence coefficient is proposed in this thesis. The technique uses different classification strategies by measuring base classifier outputs values to utilize the complementary information. Meanwhile, the execution speed is taken into account. We perform comparative analysis of five feature extraction methods and their combinations on EEG signal from BCI competition IV. Experimental results show that the proposed combination method could utilize complementary information between multiple classifiers effectively. The average kappa value of our combination method is0.63which has15.38%of rising than that of all base classifiers.
The P300event-related potential (ERP), with advantages of highly stability and not need initial training, is one of the most commonly used in BCI applications. The traditional row/column paradigm (RCP) that flashes an entire column or row of a visual matrix has succeeded in helping patients to spell words. However, the RCP remains subject to errors that slow communication, such as adjacency-distraction errors and double-flash errors. After extensive research, a new visual stimulus presentation paradigm called the submatrix-based paradigm (SBP) is proposed in this thesis. The SBP divide the whole keyboard matrix into several submatrices, and each submatrix flash in single display paradigm (SDP) mode and performs ensemble averaging method according to sequences separately. With an increasing number of sequences, SBP can detect the target submatrix and the attended item simultaneously. Compared with RCP, SBP can improve the practical bit rate by10.8%. Besides, the SBP has advantages of eliminating adjacency-distraction errors and double-flash errors, flexible division of matrix, better expansion and user acceptability.
To further improve the information transfer rate and realize online BCI system, an adaptive SBP online BCI is proposed. The adaptive algorithms employ a threshold which dynamically limits the number of sequences. Two threshold algorithms named maximum value algorithm and pseudo-kurtosis algorithm are proposed and compared in the thesis. Online experimental results show that both adaptive algorithms are effective, and the highest practical bit rate of maximum value algorithm is34.36bits/min which is9.04%higher than that of checkerboard paradigm (CBP) and11.2%higher than that of Jin's n-flash adaptive system (NFA).
在非植入式BCI研究中,基于头皮脑电(EEG)的采集方式具有无损伤、使用方便、设备便宜等优点,使得基于EEG的BCI技术具有很好的研究价值。用于BCI研究的EEG信号有多种模式,本文在充分研究各种模式特点的基础上,以运动想象脑电和P300电位为切入点,系统的研究了BCI系统构建、离线算法研究到在线实现的多个层面的问题,取得了较好的成果。
BCI的研究需要一个系统的软硬件平台,为了快速构建一套BCI系统,论文对目前的软硬件平台进行了研究。经过比较和已有实验条件,利用Neuroscan脑电采集设备和BCI2000软件系统快速构建了一套在线BCI系统。通过对BC12000的二次开发,积累了一些在线实验的经验,为实验室后续研究创造了条件。
信号处理算法将采集到的信号进行分析处理,识别出大脑意图并转换成控制指令,信号处理算法的好坏直接影响BCI系统性能。论文对BCI中的信号处理算法进行了研究,详细介绍了几种常用算法的原理和特点。针对BCI系统中存在的信息传输率较慢和脑电信号识别正确率较低的问题,对多通道四类运动想象脑电信号进行了研究,提出了一种基于共空间模式(CSP), Hilbert变换和支持向量机(SVM)的特征提取和分类算法。经过BCI竞赛数据的验证,该算法具有分类正确率高和时间开销小的特点,随着阈值的增大,平均分类正确率从87.22%提高到92.22%,但是时间开销增长到2.15倍,通过阈值的调节,在正确率和算法复杂度之间获得平衡。算法复杂度明显比采用多个SVM组合的多类分类算法要低,为实现算法的在线应用打下基础。
传统对脑电信号的识别通常是用不同的信号处理算法,然后选择一个最好的解决方案。然而,不同的信号处理算法性能存在差异,相互之间存在互补信息。为了充分利用各个信号处理算法的互补信息,论文提出了一种基于置信系数的组合分类算法,对不同分类难易程度的样本采用不同的分类策略。样本越容易分类,则组合分类算法侧重于提高算法的执行速度；样本越难分类,则组合分类算法侧重于提高算法的分类正确率。在提高算法分类能力同时兼顾算法的时间开销。经过BCI竞赛数据的验证,提出的基于置信系数的组合分类算法比分量分类器的平均分类能力提高15.4%。
基于P300的BCI是一类重要的BCI实现形式。自从Farwell和Donchin提出行列刺激范式(RCP)的P300字符输入系统以来,基于P300的BCI得到了广泛关注,研究人员从信号处理、刺激装置和实验范式等多个角度进行了广泛研究。论文则从实验范式角度进行了深入研究,提出了一种基于子矩阵的刺激范式(SBP),将整个字符矩阵划分为多个子矩阵,随机闪烁子矩阵中的一个字符,经过多次叠加,不仅可以提高脑电信号的信噪比,还能识别目标字符所在的子矩阵,显著提高了字符识别正确率和信息传输率。与RCP相比,SBP的最高实际信息传输率提升幅度达到10.8%。此外,SBP还具有不存在邻接干扰和双闪问题、子矩阵划分方式灵活、增加可选择的字符数量并不明显延长目标字符识别时间、更容易被受试者接受等优点。
为了进一步提高SBP的信息传输率并实现BCI的在线实验,论文提出了一种自适应的在线BCI系统。通过设定一个阈值来实现对P300叠加次数的动态调节,因此,阈值算法的研究是自适应算法的重要部分。论文在分析SBP原理和P300成分分布特点基础上,提出了最大值法和类峭度法两种阈值算法,均获得了较好的实验结果,其中最大值法可以获得最高34.36bits/min的实际信息传输率,比棋盘格刺激范式(CBP)的实际信息传输率提高9.04%,比Jin等人的自适应系统(NFA)的实际信息传输率提高11.2%。
Brain computer interface (BCI) builds an independent communication channel between the brain and external devices without using brain's normal output pathways of peripheral nerves and muscles, and acts as a new interactive way. BCI technology has a widely application prospect in rehabilitation, auxiliary control, neurorobotics, entertainment and so on.
In non-invasive BCI research, electroencephalography (EEG)-based BCIs are the most common for studies, because the recording method is safe, convenient and inexpensive. Various types of EEG signals have been used in BCIs. This thesis takes motor imagery EEG data and P300potential as key point, systematically describes the framework of the BCI, offline algorithm and online realization, and achieves some good results.
BCI is a complex system integrating hardware and software platforms. Now, several hardware and general-purpose software are available to build an online system rapidly. On the base of platform comparison and existing experimental condition, Neuroscan hardware devices and BCI2000software are combined to build an online BCI system. The thesis provides some online experimental experiences for future research on BCI by the secondary development of BCI2000.
Signal processing algorithm converts brain signals into control signals, the results of algorithm directly affect the performance of BCI system. The thesis presents a study on some frequently used signal processing method in detail. Due to the low information transfer rate and low recognition accuracy in BCI, the thesis proposes an algorithm based on common spatial pattern (CSP), Hilbert transformation and support vector machine (SVM) for feature extraction and classification of multi-channel four-class motor imagery EEG signals. With the datasets of BCI competition III, experimental results show that the proposed algorithm produces high classification accuracy and less time consumption, moreover, classification result can be further improved at the expense of algorithmic complexity by the threshold adjustment. With the increase of threshold, average classification accuracy is improved from87.22%to92.22%while the time consumption is increased to2.15times.
To choose the best solution, different feature extraction and classification algorithms are usually considered in BCI experiment. However, there is complementary information between different methodologies or different features that can improve the classification performance. In order to use the complementary information efficiently, the confidence coefficient is proposed to determine classification difficulty levels of samples in a classifier, and then combination of multiple classifiers based on confidence coefficient is proposed in this thesis. The technique uses different classification strategies by measuring base classifier outputs values to utilize the complementary information. Meanwhile, the execution speed is taken into account. We perform comparative analysis of five feature extraction methods and their combinations on EEG signal from BCI competition IV. Experimental results show that the proposed combination method could utilize complementary information between multiple classifiers effectively. The average kappa value of our combination method is0.63which has15.38%of rising than that of all base classifiers.
The P300event-related potential (ERP), with advantages of highly stability and not need initial training, is one of the most commonly used in BCI applications. The traditional row/column paradigm (RCP) that flashes an entire column or row of a visual matrix has succeeded in helping patients to spell words. However, the RCP remains subject to errors that slow communication, such as adjacency-distraction errors and double-flash errors. After extensive research, a new visual stimulus presentation paradigm called the submatrix-based paradigm (SBP) is proposed in this thesis. The SBP divide the whole keyboard matrix into several submatrices, and each submatrix flash in single display paradigm (SDP) mode and performs ensemble averaging method according to sequences separately. With an increasing number of sequences, SBP can detect the target submatrix and the attended item simultaneously. Compared with RCP, SBP can improve the practical bit rate by10.8%. Besides, the SBP has advantages of eliminating adjacency-distraction errors and double-flash errors, flexible division of matrix, better expansion and user acceptability.
To further improve the information transfer rate and realize online BCI system, an adaptive SBP online BCI is proposed. The adaptive algorithms employ a threshold which dynamically limits the number of sequences. Two threshold algorithms named maximum value algorithm and pseudo-kurtosis algorithm are proposed and compared in the thesis. Online experimental results show that both adaptive algorithms are effective, and the highest practical bit rate of maximum value algorithm is34.36bits/min which is9.04%higher than that of checkerboard paradigm (CBP) and11.2%higher than that of Jin's n-flash adaptive system (NFA).
引文
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