脑—机接口的特征提取和分类方法研究
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摘要
脑-机接口(brain computer interface, BCI)是指在人脑和计算机或其它电子设备之间建立的直接的交流和控制通道,它不依赖于脑的正常生理输出通路(外周神经系统及肌肉组织)。脑-机接口是一种全新的人机接口方式,是近年来脑功能研究的热点课题。脑-机接口技术可以恢复有严重运动障碍患者的正常运动功能,提高其生活质量。脑-机接口是一门多学科交叉的新兴技术,正成为脑科学、康复工程、生物医学工程及人机交互领域的前沿研究热点。
脑电(EEG)的特征提取和分类是脑-机接口技术的关键。本文对脑-机接口中脑电信号的特征提取方法进行了深入的探讨,主要包括现代功率谱估计和小波分析等。进而,深入地研究了脑-机接口系统所采用的分类方法,包括Fisher线性判别分析、神经网络(学习矢量量化网络和概率神经网络)和支持向量机等。本文利用这些特征提取方法和分类方法,对一些典型的脑-机接口数据集进行了离线分析,并建立了基于Alpha波的异步脑-机接口系统。本文的主要成果有：
1)对一个典型的采用慢皮层电位的脑-机接口系统进行了离线分析,对慢皮层电位进行了特征提取,并采用线性判别分析进行分类。该方法分类速度较快,稳定性好,取得了很好的分类效果。该实验证明,一定时间的训练对实验者掌握实验技巧,提高分类准确率有很大帮助。同时,利用Simulink建立了一个仿真模型,该模型具有很强的灵活性,可广泛用于采用慢皮层电位的脑-机接口系统。
2)提出了一种采用相对小波能量的脑电信号特征提取方法,并将该方法用于采用想象左手或右手运动的脑-机接口系统。采用分类正确率和互信息作为脑-机接口系统的评价标准,并给出了互信息的详细计算方法。分类方法采用线性判别分析和支持向量机,并对两者进行了比较分析。结果表明,相对小波能量具有比自适应AR模型系数更好的可分性；互信息具有比分类准确率更高的可靠性。
3)提出了一种采用MUSIC算法功率谱估计进行脑电特征提取的方法,并采用学习矢量量化网络进行分类。该方法比AR模型功率谱估计具有更好的可分性和稳定性。结果表明,该方法为脑-机接口的研究提供了新的思路。
4)将相对小波能量用于采用皮层脑电图的脑-机接口系统的特征提取。对采用相对小波能量得到的特征矢量,采用主分量分析进行降维。分类方法采用概率神经网络,并详细地分析了径向基函数分布密度对分类结果的影响。结果表明,该方法在分类准确率上有很大地提高。
5)建立了采用Alpha波的异步脑-机接口系统开发平台,并进行了实时在线分析。该系统的基本原理是Alpha波阻断现象,主要包括：实时脑电采集、特征提取和分类。提出了一种适合脑-机接口系统的异步工作模式,实验者可以任意选择何时启动系统,并随意的选择四个命令中的一个进行输出,是一种更加自然的人机交互方式。该系统准确率高、稳定性好、具有很高的实用性。
Brain-computer interface (BCI) is a direct communication and control channel between human brain and computer or other electronic device. It does not depend on the brain's normal output pathways of peripheral nerves and muscles. The BCI is a novel kind of human computer interface and recently it is an active topic in brain function research. BCI technology can help improve the quality of life and restore function for people with severe motor disabilities. BCI research is a multidisciplinary field and has been a hot focus in brain science, rehabilitation engineering, biomedical engineering and human computer interaction.
Feature selection and classification methods of EEG signals are two key points of BCI technology. Feature extraction methods of EEG signals using in BCI systems are discussed in this paper, mainly including modern power spectral density (PSD) and wavelet analysis. Then classification methods used in BCI systems are investigated thoroughly, mainly including Fisher linear discriminant analysis (LDA), neural network (learning vector quantization neural network and probabilistic neural network) and support vector machine. In this paper, these feature selection and classification methods are used in the off-line analysis of some typical BCI dataset, and an asynchronous BCI system based on Alpha wave is built. Main contributions of this paper include:
(1) A typical BCI system using slow cortical potential (SCP) is off-line analysed. Features are got from the SCP, and LDA is used for classification. This method has got good classification accuracy, fast speed and good stability. The result of the experiment shows that training can give much help for the subject to master skill and improve classification accuracy. At the same time, a simulation model based on Simulink is built, which is very flexible and can be widely used in SCP-based BCI system.
(2) Relative wavelet energy (RWE) used for feature selection of EEG is proposed. This method is utilized in BCI system using imaged left or right hand movement. Classification accuracy and mutual information (MI) are used for evaluation criteria of BCI system. LDA and SVM are respectively utilized for classification and compared with each other. The results of the experiment show that RWE is a good feature selection method, comparing with AAR. And MI is more reliable than classification accuracy.
(3) PSD using multiple signal classification (MUSIC) method for feature selection of EEG is proposed, and learning vector quantization (LVQ) neural network is used for classification. Comparing with AR model, the PSD using MUSIC method has better separability and stability. The experiment result shows that this method provides a new way for BCI research.
4) For BCI system using electrocorticography (ECoG) signals, feature selection method using RWE is proposed. Principal components analysis (PCA) is used to reduce the dimension of the feature vector which gets from RWE. Probabilistic neural network (PNN) is used for classification, and the influence of the spread of radial basis functions to the classification accuracy is investigated. The result of the experiment shows that this algorithm has got significant improvement on classification accuracy.
5) An asynchronous BCI system platform based on Alpha wave is built, and it is real-time analysed. The basic theory of this BCI system is alpha wave-block phenomenon. It mainly includes:real-time EEG acquisition, preprocessing, feature selection and classification. An asynchronous working mode which is suitable for BCI system is proposed. The subject can decide freely when he or she wishes to run the BCI system and chooses anyone of four commands as output. It is a more natural human computer interaction method. This system has got high classification accuracy, good stability and practicability.
脑电(EEG)的特征提取和分类是脑-机接口技术的关键。本文对脑-机接口中脑电信号的特征提取方法进行了深入的探讨,主要包括现代功率谱估计和小波分析等。进而,深入地研究了脑-机接口系统所采用的分类方法,包括Fisher线性判别分析、神经网络(学习矢量量化网络和概率神经网络)和支持向量机等。本文利用这些特征提取方法和分类方法,对一些典型的脑-机接口数据集进行了离线分析,并建立了基于Alpha波的异步脑-机接口系统。本文的主要成果有：
1)对一个典型的采用慢皮层电位的脑-机接口系统进行了离线分析,对慢皮层电位进行了特征提取,并采用线性判别分析进行分类。该方法分类速度较快,稳定性好,取得了很好的分类效果。该实验证明,一定时间的训练对实验者掌握实验技巧,提高分类准确率有很大帮助。同时,利用Simulink建立了一个仿真模型,该模型具有很强的灵活性,可广泛用于采用慢皮层电位的脑-机接口系统。
2)提出了一种采用相对小波能量的脑电信号特征提取方法,并将该方法用于采用想象左手或右手运动的脑-机接口系统。采用分类正确率和互信息作为脑-机接口系统的评价标准,并给出了互信息的详细计算方法。分类方法采用线性判别分析和支持向量机,并对两者进行了比较分析。结果表明,相对小波能量具有比自适应AR模型系数更好的可分性；互信息具有比分类准确率更高的可靠性。
3)提出了一种采用MUSIC算法功率谱估计进行脑电特征提取的方法,并采用学习矢量量化网络进行分类。该方法比AR模型功率谱估计具有更好的可分性和稳定性。结果表明,该方法为脑-机接口的研究提供了新的思路。
4)将相对小波能量用于采用皮层脑电图的脑-机接口系统的特征提取。对采用相对小波能量得到的特征矢量,采用主分量分析进行降维。分类方法采用概率神经网络,并详细地分析了径向基函数分布密度对分类结果的影响。结果表明,该方法在分类准确率上有很大地提高。
5)建立了采用Alpha波的异步脑-机接口系统开发平台,并进行了实时在线分析。该系统的基本原理是Alpha波阻断现象,主要包括：实时脑电采集、特征提取和分类。提出了一种适合脑-机接口系统的异步工作模式,实验者可以任意选择何时启动系统,并随意的选择四个命令中的一个进行输出,是一种更加自然的人机交互方式。该系统准确率高、稳定性好、具有很高的实用性。
Brain-computer interface (BCI) is a direct communication and control channel between human brain and computer or other electronic device. It does not depend on the brain's normal output pathways of peripheral nerves and muscles. The BCI is a novel kind of human computer interface and recently it is an active topic in brain function research. BCI technology can help improve the quality of life and restore function for people with severe motor disabilities. BCI research is a multidisciplinary field and has been a hot focus in brain science, rehabilitation engineering, biomedical engineering and human computer interaction.
Feature selection and classification methods of EEG signals are two key points of BCI technology. Feature extraction methods of EEG signals using in BCI systems are discussed in this paper, mainly including modern power spectral density (PSD) and wavelet analysis. Then classification methods used in BCI systems are investigated thoroughly, mainly including Fisher linear discriminant analysis (LDA), neural network (learning vector quantization neural network and probabilistic neural network) and support vector machine. In this paper, these feature selection and classification methods are used in the off-line analysis of some typical BCI dataset, and an asynchronous BCI system based on Alpha wave is built. Main contributions of this paper include:
(1) A typical BCI system using slow cortical potential (SCP) is off-line analysed. Features are got from the SCP, and LDA is used for classification. This method has got good classification accuracy, fast speed and good stability. The result of the experiment shows that training can give much help for the subject to master skill and improve classification accuracy. At the same time, a simulation model based on Simulink is built, which is very flexible and can be widely used in SCP-based BCI system.
(2) Relative wavelet energy (RWE) used for feature selection of EEG is proposed. This method is utilized in BCI system using imaged left or right hand movement. Classification accuracy and mutual information (MI) are used for evaluation criteria of BCI system. LDA and SVM are respectively utilized for classification and compared with each other. The results of the experiment show that RWE is a good feature selection method, comparing with AAR. And MI is more reliable than classification accuracy.
(3) PSD using multiple signal classification (MUSIC) method for feature selection of EEG is proposed, and learning vector quantization (LVQ) neural network is used for classification. Comparing with AR model, the PSD using MUSIC method has better separability and stability. The experiment result shows that this method provides a new way for BCI research.
4) For BCI system using electrocorticography (ECoG) signals, feature selection method using RWE is proposed. Principal components analysis (PCA) is used to reduce the dimension of the feature vector which gets from RWE. Probabilistic neural network (PNN) is used for classification, and the influence of the spread of radial basis functions to the classification accuracy is investigated. The result of the experiment shows that this algorithm has got significant improvement on classification accuracy.
5) An asynchronous BCI system platform based on Alpha wave is built, and it is real-time analysed. The basic theory of this BCI system is alpha wave-block phenomenon. It mainly includes:real-time EEG acquisition, preprocessing, feature selection and classification. An asynchronous working mode which is suitable for BCI system is proposed. The subject can decide freely when he or she wishes to run the BCI system and chooses anyone of four commands as output. It is a more natural human computer interaction method. This system has got high classification accuracy, good stability and practicability.
引文
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