Classification Accuracy of an Imagined-Movements Mental Task Set for Brain-Machine Interface

摘要：

We study the classification accuracy of a neural network (NN) classifier for a Brain-Machine Interface (BMI) system that uses an imagined-movements mental task set and compare the performance of the classifier when the user is subjected to a non-moving (static) reference frame (SRF) and to a moving (dynamic) reference frame (DRF) while performing the mental tasks. We use the band powers and power differences of electroencephalogram (EEG) signals recorded from 8 surface electrodes. Results show that tasks involving imagined movements are the most immune to the SRF-DRF switch but may not be the most appropriate BMI protocol for BMI classification.

关键词： Brain-Machine Interface static and dynamic reference classification accuracy imagined movement

作者: Michael Lochinvar S. Abundo Eliezer A. Marco Gino Francisco R. Mempin Marc Caesar R. Talampas Luis G. Sison

作者单位: Instrumentation, Robotics, and Control Laboratory Department of Electrical and Electronics Engineering University of the Philippines, Diliman

会议类型: 国际会议

会议名称: The 2nd International Conference on Bioinformatics and Biomedical Engineering(iCBBE 2008)(第二届生物信息与生物医学工程国际会议)

会议地点: 上海

会议语种:英文

页码: 1264-1267

在线出版日期: 2008-05-16（万方平台首次上网日期，不代表论文的发表时间）