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中文摘要：
特征提取和特征分类是脑机接口中模式识别过程中两个关键的环节。首先,针对脑电信号的非平稳特性,提出基于小波变换系数、系数均值及小波熵相结合的特征提取方法,该方法在特征中加入了脑电信号的能量信息。实验证明,通过该方法获取的P300信号特征量能够更好地表达脑电信号中的瞬变成分,进一步提高了识别率;其次,在模式识别方面,改进了基于自训练半监督的支持向量机算法和基于自训练半监督的K均值聚类算法。在BCI2003竞赛数据集上的实验表明,相比于传统的BP神经网络,两种改进的分类算法在获得了更高的识别率的同时,能够将特征量维数降低一个数量级,明显提高了训练收敛速度,有效增加了基于脑电信号的实时BCI系统的可实现性。
英文摘要：
Feature extraction and feature classification are the most important parts in pattern recognition in BCI.Since brain signals are non-stationary,we put forward the idea of combining wavelet transform coefficients,coefficient averages and wavelet entropy as the feature vectors,which joins the energy information of brain signal.Through this method,features extracted from the P300 signal could express the transient components of the signal to effectively increase the classification accuracy with the aim to control the machines through brain activity.In feature classification,we improved two self-training semi-supervised algorithms based on support vector machine（SVM） and K-means.In the experiment of BCI2003 datasets,compared with traditional BP neutral network,it was showed that the two proposed methods improve the classification accuracy with much lower dimension of the extracted features and shorter convergence process,which makes real-time BCI system possible.
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