基于脑电节律特征优化与同步性的运动意愿解析
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摘要
脑机接口技术的目标是为在大脑和外部设备之间建立一条不依赖外周神经及肌肉的直接控制及感知通道。这种技术有望恢复残疾及瘫痪患者的运动交流能力并为人类提供新的信息交互方式。脑机接口的基础是对检测到的神经信号进行解码以判断大脑活动的真实意愿。对基于脑电信号的脑机接口来说,所得到的信号属于宏观场电位,基本形式体现为各种节律信号的综合。因此,对脑电节律特性的分析和理解,以及对节律信号的解码是相关脑机接口研究的重要基础。
本论文以脑机接口中的节律脑电信号解码为研究目标,重点研究运动相关节律信号多频率时间成分共存特性及信号同步特征提取方法,旨在将其用于运动意愿的解码。论文的主要创新点包括提出了综合脑电节律信号的频率、时间、空间特征的优化方法,在优化中采用自适应策略以平衡脑机接口识别精度和泛化能力,以及提出并验证了一种基于网络特性的脑电节律同步性特征。
在脑电节律成分优化方法中,论文研究了脑电节律信号的频域、时域、空域信息的相互关系,在现有的多种特征筛选方法的基础上提出了一种同时对信号的频率-时间-空间特征进行综合利用的方法,以加强对运动相关脑电信号模式的识别。在该方法中,论文分析了频-时-空模式识别的精度和脑机接口泛化能力之间的平衡问题,通过一种数据驱动的算法得到优化的特征数据以同时提升这两个指标。在采用真实的运动及运动想象脑电的实验中,该方法相对于原有的基于共空域模式的脑电识别方法可以更有效地对不同受试者的运动相关脑电进行个性化的识别,两类实验的平均准确率分别相对提高10%和6%以上,有助于提高脑机接口系统的实用性。
在节律信号同步化方面论文主要关注多通道信号同步特征。脑电信号的空间特征不仅体现为不同时频成分的节律在空间中的分布,还包含了不同位置信号之间的相关信息。通道间的同步性一定程度上反映了不同脑区间的同步相关特性,而后者与大脑的许多重要功能息息相关。但在现有的脑机接口解码算法中对脑电同步性的整体分析少见报道,本论文采用一种基于脑电通道间同步性网络的信号特征提取方法。该方法先将各个通道间的节律同步信息网络化,然后通过计算网络属性来获得反映脑电信号同步性的特征。在此项研究中,为了更好地评估同步化分析方法,论文还对节律信号的同步性机制模型进行了研究,对常用的节律活动模型进行了改进使之更符合真实的神经组织结构。由此获得的模拟数据被用于同步化方法的验证和参数设置。
在以上节律信号研究的基础上,论文还将脑电同步性分析方法和脑电成分优化方法结合。通过优化特征信息确定了用于同步性分析的信号的时频范围,从而提高了算法的解码性能。对真实脑电数据的解码实验显示新的特征提取方法能有效区别不同的运动意愿。
Brain-computer interface (BCI) system can provide a direct control or sensory channel that do not rely on peripheral neural and muscular system between brain and external equipments. BCI technologies are expected to restore the motor functionality of patients with paralytic injury or disease, and to provide humans with new way of interaction. The base of BCI technologies are brain intention identification by means of neural signal decoding. For BCIs based on electroencephalogram (EEG), the signal utilized is a synthesis of multiple rhythmic field potentials, so the analysis for understanding the properties of EEG rhythm plays an important role in related BCI decoding methods.
This dissertation focuses on the property of rhythmic signal components and methods for its feature extraction. The resulting algorithm is used in motor intention decoding.
In the analysis of EEG rhythm components, the relationships between information frequency, temporal and spatial EEG domains have been studied. The dissertation proposed a comprehensive method to optimize the features in all three domains simultaneously. The new method is designed to achieve a better tradeoff between EEG pattern recognition accuracy and generalization capacity of the algorithm, as the proposed method used a data-driven method to achive varied accuracy for different features. The experiments with motor execution and motor imagery EEG data showed that the proposed algorithm can achieve a better user-specific recognition of the motor-related EEG patterns (average rate of correct recognition improved more than 10% and 6% for two experiments), while at the same time remains the system's generalization capacity-which is two important aspects of BCI application.
The synchronization feature of multi-channel EEG is also studied in this dissertation, as it embodies spatial properties of EEG besides component distribution. Synchronization between EEG channels reflects neural synchrony between brain regions, and neural synchrony is believed to support a series of important brain functionality. However there are few reports about BCI algorithm using overall synchronization pattern. A feature extraction method based on connectivity network of synchrony is proposed, which first calculates the synchronization value between electrode pairs, then generates the network topography measures to discribe the network synchronization pattern. In order to evaluate this method, the dissertation also proposed a modified rhythmic signal synchonization model. The simulation data generated with the model is used to set the parameters of the feature extraction method.
Based on the above studies of rhythmic signal, the synchronization feature method is combined with the EEG component feature optimization to limite signal time-frequency range. Tests with real motor-related EEG data showed that the combination would largely improve the performance of synchronization method.
本论文以脑机接口中的节律脑电信号解码为研究目标,重点研究运动相关节律信号多频率时间成分共存特性及信号同步特征提取方法,旨在将其用于运动意愿的解码。论文的主要创新点包括提出了综合脑电节律信号的频率、时间、空间特征的优化方法,在优化中采用自适应策略以平衡脑机接口识别精度和泛化能力,以及提出并验证了一种基于网络特性的脑电节律同步性特征。
在脑电节律成分优化方法中,论文研究了脑电节律信号的频域、时域、空域信息的相互关系,在现有的多种特征筛选方法的基础上提出了一种同时对信号的频率-时间-空间特征进行综合利用的方法,以加强对运动相关脑电信号模式的识别。在该方法中,论文分析了频-时-空模式识别的精度和脑机接口泛化能力之间的平衡问题,通过一种数据驱动的算法得到优化的特征数据以同时提升这两个指标。在采用真实的运动及运动想象脑电的实验中,该方法相对于原有的基于共空域模式的脑电识别方法可以更有效地对不同受试者的运动相关脑电进行个性化的识别,两类实验的平均准确率分别相对提高10%和6%以上,有助于提高脑机接口系统的实用性。
在节律信号同步化方面论文主要关注多通道信号同步特征。脑电信号的空间特征不仅体现为不同时频成分的节律在空间中的分布,还包含了不同位置信号之间的相关信息。通道间的同步性一定程度上反映了不同脑区间的同步相关特性,而后者与大脑的许多重要功能息息相关。但在现有的脑机接口解码算法中对脑电同步性的整体分析少见报道,本论文采用一种基于脑电通道间同步性网络的信号特征提取方法。该方法先将各个通道间的节律同步信息网络化,然后通过计算网络属性来获得反映脑电信号同步性的特征。在此项研究中,为了更好地评估同步化分析方法,论文还对节律信号的同步性机制模型进行了研究,对常用的节律活动模型进行了改进使之更符合真实的神经组织结构。由此获得的模拟数据被用于同步化方法的验证和参数设置。
在以上节律信号研究的基础上,论文还将脑电同步性分析方法和脑电成分优化方法结合。通过优化特征信息确定了用于同步性分析的信号的时频范围,从而提高了算法的解码性能。对真实脑电数据的解码实验显示新的特征提取方法能有效区别不同的运动意愿。
Brain-computer interface (BCI) system can provide a direct control or sensory channel that do not rely on peripheral neural and muscular system between brain and external equipments. BCI technologies are expected to restore the motor functionality of patients with paralytic injury or disease, and to provide humans with new way of interaction. The base of BCI technologies are brain intention identification by means of neural signal decoding. For BCIs based on electroencephalogram (EEG), the signal utilized is a synthesis of multiple rhythmic field potentials, so the analysis for understanding the properties of EEG rhythm plays an important role in related BCI decoding methods.
This dissertation focuses on the property of rhythmic signal components and methods for its feature extraction. The resulting algorithm is used in motor intention decoding.
In the analysis of EEG rhythm components, the relationships between information frequency, temporal and spatial EEG domains have been studied. The dissertation proposed a comprehensive method to optimize the features in all three domains simultaneously. The new method is designed to achieve a better tradeoff between EEG pattern recognition accuracy and generalization capacity of the algorithm, as the proposed method used a data-driven method to achive varied accuracy for different features. The experiments with motor execution and motor imagery EEG data showed that the proposed algorithm can achieve a better user-specific recognition of the motor-related EEG patterns (average rate of correct recognition improved more than 10% and 6% for two experiments), while at the same time remains the system's generalization capacity-which is two important aspects of BCI application.
The synchronization feature of multi-channel EEG is also studied in this dissertation, as it embodies spatial properties of EEG besides component distribution. Synchronization between EEG channels reflects neural synchrony between brain regions, and neural synchrony is believed to support a series of important brain functionality. However there are few reports about BCI algorithm using overall synchronization pattern. A feature extraction method based on connectivity network of synchrony is proposed, which first calculates the synchronization value between electrode pairs, then generates the network topography measures to discribe the network synchronization pattern. In order to evaluate this method, the dissertation also proposed a modified rhythmic signal synchonization model. The simulation data generated with the model is used to set the parameters of the feature extraction method.
Based on the above studies of rhythmic signal, the synchronization feature method is combined with the EEG component feature optimization to limite signal time-frequency range. Tests with real motor-related EEG data showed that the combination would largely improve the performance of synchronization method.
引文
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