基于运动想象的脑电信号分类与脑机接口技术研究
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摘要
脑机接口是一种不依赖于大脑外周神经与肌肉组成的正常输出通路的通讯控制系统,由于该技术具有巨大的理论研究价值以及实际应用前景,成为近年来生物医学工程领域中研究的热点。对运动想象脑电信号进行分类研究,是脑机接口研究领域的一个重要分支。
根据河北省自然科学基金资助项目“基于EEG的脑机接口技术研究”(编号:E2006000034)的要求,本文对运动想象脑电信号提取的实验方案进行了设计,对采集得到的脑电数据分别提取能量特征以及复杂度特征进行了分类研究,同时建立了实时在线脑机接口系统,并对脑机接口技术的实际应用进行了探索性研究。主要内容如下:
1.设计了基于想象左手、右手和脚部运动的脑电信号采集实验方案。
对脑电信号产生原理,运动想象脑电信号的特点进行详细的阐述,根据脑电信号记录仪器的功能及特点,设计了提取运动想象过程中脑电信号的实验方案,提取得到了对应三种不同运动想象任务的脑电数据,为后续的离线分析处理提供了数据基础。
2.进行了以脑电信号能量为特征的分类研究。
对应在线脑机接口研究需要,对脑电信号进行平稳性假设,采用傅立叶变换提取特定频率段的能量特征,将该特征作为分类依据,进行了分类研究。所采用的特征提取方法、特征频段能量确定方法及分类结果都被应用于在线脑机接口系统设计中。
根据脑电信号非线性、非平稳的特点,采用希尔伯特—黄变换对信号进行了时频分析,提取综合考虑时间-频率-空间信息的能量特征作为分类依据。通过对该方法提取得到特征采用过滤和封装混合特征选择方法进行优化处理。分类结果证明,该特征提取方法以及特征选择方法的有效性。
3.进行了以脑电信号的非线性复杂度为特征的分类研究。
以非线性动力学指标-样本熵作为不同运动想象任务时脑电信号的特征,以支持向量机作为分类器,进行了分类研究。将特征子集与分类器参数联合优化的思想应用于分类研究中,为解决分类速度和分类精度之间的矛盾提供了一种可行方法,并获得了较好的分类效果。
4.建立了在线脑机接口系统。
在前期离线数据分析的基础上,设计了基于运动想象脑电信号的在线脑机接口系统。针对目前在线脑机接口系统难以识别“工作”/“空闲”状态的问题,提出了将闭眼alpha波幅值显著增加的现象作为切换不同状态的标志的方法,在在线脑机接口系统中设计了状态监测模块。实验证明,经过训练的使用者在该在线脑机接口平台上可以自如的在不同状态间进行切换,并且能以很高的分类正确率发出控制命令。同时还对脑机接口技术应用于实际进行了初步的探索性研究,利用在线脑机接口平台完成了对模拟轮椅的电动小车的控制。
Brain computer interface (BCI) is a communication system that does not depend on the brain’s normal output pathways of peripheral nerves and muscles. This technology becomes the hot issue of the biomedical engineering research field in nowadays, because it has great value in theory research and practical application. Classification the electroencephalogram (EEG) recorded during different motor imagery is a main branch of BCI research.
According to the Natural Science Foundation of Hebei Province under Grant E2006000034, experiments to record the EEG during different motor imageries were designed. Classification based on power and complicity was made by using the EEG data gained from experiments. Then, an on-line BCI system was established and this system was attempted to be put into real application. The main points of this paper are:
1. In order to fulfill the demand of the research, experiments to record EEG during imagery left hand movement, right hand movement and foot movement were designed.
Based on the study of EEG production and the characters of motor imagery EEG and the consideration of EEG recording system’s functions, the experiments were projected. Three different EEG of motor imageries were gained though experiments and these data were used in the following research.
2. Power character of EEG was extracted and used in classification
EEG was provided as a stationary signal for the request of on-line BCI research. Fourier transform was used to extract power feature at peculiar frequency band, and the feature was used in classification. The method to extract power character, the measure to find the peculiar frequency band and the results of classification will contribute to the design of an on-line BCI system.
According the fact that EEG is non-stationary and non-linear, time-frequency analysis with Hilbert-Huang transform (HHT) was made. Power feature comprehensive considered about temporal-frequency-spatial information was used in classification. Features extracted by the HHT were optimized using filter and wrapper hybrid method. The results of classification proved the validity of the feature extract method and optimize method.
3. Non-linear complexity of EEG was extracted and used in classification
A non-linear dynamic method called Sample Entropy (SampEn) was applied to extract the feature of EEG signals from different motor imagery. Support vector machine used as classifier. The result shows that it can reach a better effect for motor imagery’s classification to extract the feature by sample entropy. The novel idea which combined optimize the feature subset and parameter of the classifier was investigated in classify research. It provided a route to resolve the contradiction between the speed and accuracy during classification. A better classification result was gained.
4. An on-line BCI system was established
Based on the analysis of off-line data, an on-line BCI system using motor imagery EEG was designed. Aimed at the problem that present on-line BCI system can not recognize“idle”and“active”state, a method using the fact that close eyes can increase amplitude of alpha wave as a sign to switch different state was provided. A module which function is to monitor the state and switch different state was designed for the system. The testing result shows that, after a period of training, the subject can switch freely among different states by using this on-line BCI system. Control commands were sent out by the subject through this system at an especial high accuracy. An exploration research about the on-line BCI system’s real applications was made. A mini electric car which simulates a wheelchair was controlled through this on-line BCI system.
根据河北省自然科学基金资助项目“基于EEG的脑机接口技术研究”(编号:E2006000034)的要求,本文对运动想象脑电信号提取的实验方案进行了设计,对采集得到的脑电数据分别提取能量特征以及复杂度特征进行了分类研究,同时建立了实时在线脑机接口系统,并对脑机接口技术的实际应用进行了探索性研究。主要内容如下:
1.设计了基于想象左手、右手和脚部运动的脑电信号采集实验方案。
对脑电信号产生原理,运动想象脑电信号的特点进行详细的阐述,根据脑电信号记录仪器的功能及特点,设计了提取运动想象过程中脑电信号的实验方案,提取得到了对应三种不同运动想象任务的脑电数据,为后续的离线分析处理提供了数据基础。
2.进行了以脑电信号能量为特征的分类研究。
对应在线脑机接口研究需要,对脑电信号进行平稳性假设,采用傅立叶变换提取特定频率段的能量特征,将该特征作为分类依据,进行了分类研究。所采用的特征提取方法、特征频段能量确定方法及分类结果都被应用于在线脑机接口系统设计中。
根据脑电信号非线性、非平稳的特点,采用希尔伯特—黄变换对信号进行了时频分析,提取综合考虑时间-频率-空间信息的能量特征作为分类依据。通过对该方法提取得到特征采用过滤和封装混合特征选择方法进行优化处理。分类结果证明,该特征提取方法以及特征选择方法的有效性。
3.进行了以脑电信号的非线性复杂度为特征的分类研究。
以非线性动力学指标-样本熵作为不同运动想象任务时脑电信号的特征,以支持向量机作为分类器,进行了分类研究。将特征子集与分类器参数联合优化的思想应用于分类研究中,为解决分类速度和分类精度之间的矛盾提供了一种可行方法,并获得了较好的分类效果。
4.建立了在线脑机接口系统。
在前期离线数据分析的基础上,设计了基于运动想象脑电信号的在线脑机接口系统。针对目前在线脑机接口系统难以识别“工作”/“空闲”状态的问题,提出了将闭眼alpha波幅值显著增加的现象作为切换不同状态的标志的方法,在在线脑机接口系统中设计了状态监测模块。实验证明,经过训练的使用者在该在线脑机接口平台上可以自如的在不同状态间进行切换,并且能以很高的分类正确率发出控制命令。同时还对脑机接口技术应用于实际进行了初步的探索性研究,利用在线脑机接口平台完成了对模拟轮椅的电动小车的控制。
Brain computer interface (BCI) is a communication system that does not depend on the brain’s normal output pathways of peripheral nerves and muscles. This technology becomes the hot issue of the biomedical engineering research field in nowadays, because it has great value in theory research and practical application. Classification the electroencephalogram (EEG) recorded during different motor imagery is a main branch of BCI research.
According to the Natural Science Foundation of Hebei Province under Grant E2006000034, experiments to record the EEG during different motor imageries were designed. Classification based on power and complicity was made by using the EEG data gained from experiments. Then, an on-line BCI system was established and this system was attempted to be put into real application. The main points of this paper are:
1. In order to fulfill the demand of the research, experiments to record EEG during imagery left hand movement, right hand movement and foot movement were designed.
Based on the study of EEG production and the characters of motor imagery EEG and the consideration of EEG recording system’s functions, the experiments were projected. Three different EEG of motor imageries were gained though experiments and these data were used in the following research.
2. Power character of EEG was extracted and used in classification
EEG was provided as a stationary signal for the request of on-line BCI research. Fourier transform was used to extract power feature at peculiar frequency band, and the feature was used in classification. The method to extract power character, the measure to find the peculiar frequency band and the results of classification will contribute to the design of an on-line BCI system.
According the fact that EEG is non-stationary and non-linear, time-frequency analysis with Hilbert-Huang transform (HHT) was made. Power feature comprehensive considered about temporal-frequency-spatial information was used in classification. Features extracted by the HHT were optimized using filter and wrapper hybrid method. The results of classification proved the validity of the feature extract method and optimize method.
3. Non-linear complexity of EEG was extracted and used in classification
A non-linear dynamic method called Sample Entropy (SampEn) was applied to extract the feature of EEG signals from different motor imagery. Support vector machine used as classifier. The result shows that it can reach a better effect for motor imagery’s classification to extract the feature by sample entropy. The novel idea which combined optimize the feature subset and parameter of the classifier was investigated in classify research. It provided a route to resolve the contradiction between the speed and accuracy during classification. A better classification result was gained.
4. An on-line BCI system was established
Based on the analysis of off-line data, an on-line BCI system using motor imagery EEG was designed. Aimed at the problem that present on-line BCI system can not recognize“idle”and“active”state, a method using the fact that close eyes can increase amplitude of alpha wave as a sign to switch different state was provided. A module which function is to monitor the state and switch different state was designed for the system. The testing result shows that, after a period of training, the subject can switch freely among different states by using this on-line BCI system. Control commands were sent out by the subject through this system at an especial high accuracy. An exploration research about the on-line BCI system’s real applications was made. A mini electric car which simulates a wheelchair was controlled through this on-line BCI system.
引文
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