High Accuracy Classification of EEG Signal

Improving classification accuracy is a key issue to advancing brain computer interface (BCI) research from laboratory to real world applications. This article presents a high accuracy EEG signal classification method using single trial EEG signal to detect left and right finger movement. We apply an optimal temporal filter to remove irrelevant signal and subsequently extract key features from spatial patterns of EEG signal to perform classification. Specifically, the proposed method transforms the original EEG signal into a spatial pattern and applies the RBF feature selection method to generate robust feature. Classification is performed by the SVM and our experimental result shows that the classification accuracy of the proposed method reaches 90% as compared to the current reported best accuracy of 84%.

• Type: Conference
• Year: 2004
• Where: ICPR
• Authors: Chng Eng Siong, Cuntai Guan, Jiankang Wu, M. Thulasidas, S. Ranganatha, Wenjie Xu
• Keywords: Accuracy Eeg Signal, Computer Vision, ICPR 2004, Original Eeg Signal, Trial Eeg Signal