A FRAMEWORK OF COMMON SPATIAL PATTERNS BASED ON SUPPORT VECTOR DECOMPOSITION MACHINE

摘要：

In the study of Brain-computer Interfaces (BCI), the techniques of feature extraction and classification play an important role, especially in classifying single-trial electroencephalogram (EEG). In previous research, many researchers have solved these problems in two separate phases: firstly use techniques such as singular value decomposition and common spatial pattern to extract features; then design classification such as linear discriminant analysis or support vector machine. In this paper, we show a framework that combines common spatial patterns (CSP) and support vector machine (SVM) to analyze single-trial EEG/ECoG dataset We demonstrated experimental results in the data set I of BCI Competition 2005 analysis with this method which gets a high level of classification accuracy on the test set.

关键词： EEG BCI CSP SVDM

作者: KAI YIN JIN WU JIA-CAI ZHANG

作者单位: College of Information Science and Technology, Beijing Normal University, 100875 State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, 100875

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 3434-3438

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）