MEG Classification Based on Band Power and Statistical Characteristics

摘要：

　　With the work of magnetoencephalography (MEG)classification in brain-computer interface (BCI), a feature extraction method of frequency band power and statistical characteristics was proposed.On the basis of spectrum analysis for the two subjects experimental MEG data, frequency band powers of 0.5~6Hz for S1 and 10~25Hz for S2 were extracted as features for the two subjects, together with the statistical characteristics of mean for S1/S2 and standard deviation for S1,finally, the features were classified with linear discriminate analysis function directly and secondly, the results showed that the average classification accuracy was 54.38% which was higher than the achievement of BCI competition winner, Therefore, the frequency band power and statistical characteristics are effective features for MEG signals and the research of this paper gives MEG-based BCls a beneficial complement.

关键词： magnetoencephalography (MEG) brain-computer interface (BCI) band power statistical characteristics discriminate analysis

作者: Shiyu Yan Qingwen Yu Hong Wang

作者单位: School of Mechanical Engineering & Automation Northeastern University Shenyang, China

会议类型: 国际会议

会议名称: The 12th Web Information System and Application Conference第十二届全国Web信息系统及其应用学术会议(WISA2015)、全国第十次语义Web 与本体论学术研讨会(SWON2015)、全国第九次电子政务技术及应用学术研讨会(EGTA2015)

会议地点: 济南

会议语种:英文

页码: 255-258

在线出版日期: 2015-09-11（万方平台首次上网日期，不代表论文的发表时间）