Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (3): 771-774.DOI: 10.11772/j.issn.1001-9081.2014.03.0771

• Artificial intelligence • Previous Articles     Next Articles

Research of asynchronous reading imitating of brain-computer interface

CAO Qiaoling1,2,GUAN Jinan1,2   

  1. 1. College of Biomedical Engineering, South-Central University for Nationalities, Wuhan Hubei 430074, China;
    2. Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, South-Central University for Nationalities, Wuhan Hubei 430074, China
  • Received:2013-09-22 Revised:2013-11-13 Online:2014-03-01 Published:2014-04-01
  • Contact: CAO Qiaoling
  • Supported by:

    National Natural Science Foundation



  1. 1. 中南民族大学 认知科学国家民委重点实验室,武汉430074
    2. 中南民族大学 生物医学工程学院,武汉430074
  • 通讯作者: 曹巧玲
  • 作者简介:曹巧玲(1986-),女,河北邯郸人,硕士研究生,主要研究方向:生物信号处理、模式识别、脑-计算机接口;官金安(1965-),男,湖北武汉人,教授,博士,主要研究方向:生物信息处理、模式识别。
  • 基金资助:



Reading imitating of Brain-Computer Interface (BCI) works on synchronous mode, but in practice users want to switch between "work" state and "idle" state freely, namely asynchrony. Therefore, a closing-eyes fixed time as the switch between the two states was proposed to solve the problem. Firstly, an experimental scheme was put forward, then the features of Electroencephalography (EEG) signal were extracted in time and frequency domains respectively, features of time domain were classified by Support Vector Machine (SVM) and the K-means algorithm, and features of frequency domain were classified by SVM. The highest recognition rates of time domain were 95% and 89.17%, the average time needed for classification were 1.89s and 0.11s respectively. The highest and the average recognition of frequency domain rate were 86.25% and 81.875% respectively. The experimental results show that this scheme can achieve the goal of switching the two states freely.

Key words: asynchrony, Brain-Computer Interface (BCI), Support Vector Machine (SVM), feature extraction, pattern recognition



关键词: 异步, 脑机接口, 支持向量机, 特征提取, 模式识别

CLC Number: