摘要
针对脑电信号的常用识别方法都基于监督型分类算法,需要一定数量的训练数据对分类器进行训练,无法满足实时应用的要求。提出基于数据点密度大小和马氏距离的改进模糊C-均值(FCM)非监督分类算法,对2003年第二届BCI大赛脑电信号分类。首先采用经验模式分解(EMD)算法对脑电信号进行分解,提取相应特征值,再经改进的FCM算法对输入的特征值进行分类。实验结果证明了改进算法在脑电信号分类应用中的可行性和有效性。
Most of the popular EEG classifiers need to be supervised and their parameters have to be trained by a number of train dala in advance. That ＇s the reason why they cannot be used in the real time circumstances. In this paper, a new FCM unsupervised classification algorithm is proposed which is based on the density size of data dot and mahalanobis distance. Then, the algorithm is used to classify the EEG signals from the database of the second session of 2003 BCI competition. The EMD algorithm is used to decompose the EEG and extract the characteristic values, and then these values are classified by the proposed FCM algorithm. The experimental results show the algorithm＇s feasibility and validity in the EEG classification field.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2014年第6期83-89,共7页
Journal of Chongqing University
基金
四川省教育厅重点项目(2013SZA0153)
四川省应用基础研究计划项目(2013SZZZ026)
关键词
脑机接口
经验模式分解
模糊聚类
马氏距离
brain-computer interface （BCI）
empirical mode decomposition （EMD）
fuzzy clustering
Mahalanobis distance
