生物表面电信号建模、分析及其在人机交互中的应用
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摘要
生物表面电信号是生物体自主意识的体现。目前，用于人机交互的生物表面电信号源主要有脑电和肌电两种。然而，研究人员对生物电信号的了解还不够充分。信号本身的特性和采集过程各类噪音的引入使得目前基于生物电信号的人机交互仍处于实验室研究阶段，离实际应用还有一段距离。本文主要研究肌电和脑电两种形式的生物表面电信号在人机交互中的应用。在脑机接口中则以想象运动以及稳态视觉诱发电位两种模式作为研究对象。本文主要的工作如下：
在基于想象运动的脑机接口方面，本文首先针对共空域模式算法的泛化能力展开研究，通过线性混合模型仿真比较了各个因素对算法泛化能力的影响。其次，将共空域模式算法扩展到少通道情况下的应用，使其不仅能够在少通道的条件下运行并具备处理频域信息的能力。最后通过在线平台的搭建验证其有效性。
在基于稳态视觉诱发电位的脑机接口方面，本文首先通过实验比较了不同频率下占空比对于稳态视觉诱发电位的影响。进而在一系列实验假设的基础上，通过耦合的神经集群模型对其进行建模。最后通过在线实验验证了实际算法的有效性。
对于肌电信号，考虑到肌肉分布特点以及肌电信号传播时的相互干扰，本文将脑机接口中常用的空间滤波算法引入肌电信号的处理中，通过共空频域模式算法对通道间空间频域信息的联合处理来提高动作的分类识别率。此外，本文还通过肌电信号对日常生活中常用的手写动作进行识别。采用动态时间规整技术对肌电手写信号进行整体识别。
The surface bioelectrical signal is the refection of the self-consciousness of alife entity. Currently two kinds of the surface bioelectrical signals, named Elec-troencephalography (EEG) and Electromyography (EMG), are typically used forHuman-Computer Interaction (HCI). However, the understanding of the bioelec-trical signal is still not clear. Some characteristics of the signal itself and theintroduction of various types of noise in the acquisition process make the surfacebioelectrical signals based HCI is still in the laboratory research stage. Thereare still some problems for the practical application. The research in this thesisconsiders the application of HCI with two sources of the surface bioelectric sig-nals, which are EEG and EMG. Motor imagery and Steady State Visual EvokedPotential (SSVEP) are considered as the research objects in Brain ComputerInterface (BCI). The main works are as follows,
In the issue of the motor imagery based BCI, we frstly study the general-ization ability of the Common Spatial Pattern (CSP) algorithm. By applyingthe linear mixture model, simulation data is used to compare the impact of var-ious factors on the generalization ability of the algorithm. Secondly, we extendCSP algorithm to less channel condition, and make the algorithm not only havethe ability to run in the less channel condition but also be able to deal withfrequency-domain information. Finally, the realization of the online platformvalidates the efectiveness.
In the issue of the SSVEP based BCI, we frstly discuss the infuence of theduty cycle on SSVEP through experiments. Furthermore, based on a series ofexperiments and assumptions, we simulate the generation of SSVEP by coupledneural mass model. Finally, we also use the online platform to validate the efectiveness of the system.
For EMG signal, considering the muscle distribution characteristics andcrosstalk problem of EMG signals in the progress of propagation, we introducethe commonly used spatial flter methods in BCI into EMG signal processing.Using Common Spatio-Spectral Pattern algorithm, the classifcation result is im-proved by the use of both spatial and spectral information. Finally, we also tryto identify the handwriting movements used in daily life by EMG. Dynamic timewarping algorithm are used for the overall recognition of handwritten signal ofthe EMG.
在基于想象运动的脑机接口方面，本文首先针对共空域模式算法的泛化能力展开研究，通过线性混合模型仿真比较了各个因素对算法泛化能力的影响。其次，将共空域模式算法扩展到少通道情况下的应用，使其不仅能够在少通道的条件下运行并具备处理频域信息的能力。最后通过在线平台的搭建验证其有效性。
在基于稳态视觉诱发电位的脑机接口方面，本文首先通过实验比较了不同频率下占空比对于稳态视觉诱发电位的影响。进而在一系列实验假设的基础上，通过耦合的神经集群模型对其进行建模。最后通过在线实验验证了实际算法的有效性。
对于肌电信号，考虑到肌肉分布特点以及肌电信号传播时的相互干扰，本文将脑机接口中常用的空间滤波算法引入肌电信号的处理中，通过共空频域模式算法对通道间空间频域信息的联合处理来提高动作的分类识别率。此外，本文还通过肌电信号对日常生活中常用的手写动作进行识别。采用动态时间规整技术对肌电手写信号进行整体识别。
The surface bioelectrical signal is the refection of the self-consciousness of alife entity. Currently two kinds of the surface bioelectrical signals, named Elec-troencephalography (EEG) and Electromyography (EMG), are typically used forHuman-Computer Interaction (HCI). However, the understanding of the bioelec-trical signal is still not clear. Some characteristics of the signal itself and theintroduction of various types of noise in the acquisition process make the surfacebioelectrical signals based HCI is still in the laboratory research stage. Thereare still some problems for the practical application. The research in this thesisconsiders the application of HCI with two sources of the surface bioelectric sig-nals, which are EEG and EMG. Motor imagery and Steady State Visual EvokedPotential (SSVEP) are considered as the research objects in Brain ComputerInterface (BCI). The main works are as follows,
In the issue of the motor imagery based BCI, we frstly study the general-ization ability of the Common Spatial Pattern (CSP) algorithm. By applyingthe linear mixture model, simulation data is used to compare the impact of var-ious factors on the generalization ability of the algorithm. Secondly, we extendCSP algorithm to less channel condition, and make the algorithm not only havethe ability to run in the less channel condition but also be able to deal withfrequency-domain information. Finally, the realization of the online platformvalidates the efectiveness.
In the issue of the SSVEP based BCI, we frstly discuss the infuence of theduty cycle on SSVEP through experiments. Furthermore, based on a series ofexperiments and assumptions, we simulate the generation of SSVEP by coupledneural mass model. Finally, we also use the online platform to validate the efectiveness of the system.
For EMG signal, considering the muscle distribution characteristics andcrosstalk problem of EMG signals in the progress of propagation, we introducethe commonly used spatial flter methods in BCI into EMG signal processing.Using Common Spatio-Spectral Pattern algorithm, the classifcation result is im-proved by the use of both spatial and spectral information. Finally, we also tryto identify the handwriting movements used in daily life by EMG. Dynamic timewarping algorithm are used for the overall recognition of handwritten signal ofthe EMG.
引文
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