收录:
摘要:
Feature extraction plays an important role in brain computer interface system that significantly affects the success of brain signal classification. In this paper, a feature extraction method of electroencephalographic (EEG) signals based on Extreme Learning Machine auto-encoder (ELM-AE) is applied. Firstly, the original data is classified by Extreme Learning Machine (ELM) and the number of hidden layer's neuron with the highest accuracy is selected as the dimension of feature extraction. Then, ELM-AE's output weight learns to represent the features of the original data. Finally, the features are classified by Support Vector Machine (SVM) classifier. Experiment result shows the efficiency of our method for both the speed of feature extraction and the accuracy of the classification for data set la, which is a typical representative of one kind of BCI competition 2003 data.
关键词:
通讯作者信息:
电子邮件地址: