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Classification of Hemodynamic Responses Associated With Force and Speed Imagery for a Brain-Computer Interface.

Functional near-infrared spectroscopy (fNIRS) is an emerging optical technique, which can assess brain activities associated with tasks. In this study, six participants were asked to perform three imageries of hand clenching associated with force and speed, respectively. Joint mutual information (JM...

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Published in:Journal of Medical Systems Vol. 39; no. 5; pp. 1 - 9
Main Authors: Yin, Xuxian, Xu, Baolei, Jiang, Changhao, Fu, Yunfa, Wang, Zhidong, Li, Hongyi, Shi, Gang
Format: equations & formulas pictorial research tables/charts Journal Article
Published: Springer Nature May2015
Online Access:View this record in EBSCOhost
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      dt: May2015
      vid: 39
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      pub: Springer Nature
      place: New York, New York
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        atl: Classification of Hemodynamic Responses Associated With Force and Speed Imagery for a Brain-Computer Interface.
      aug:
        au:
          Yin, Xuxian
          Xu, Baolei
          Jiang, Changhao
          Fu, Yunfa
          Wang, Zhidong
          Li, Hongyi
          Shi, Gang
        affil: State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016 Peoples Republic of China
      sug:
        subj:
          Hemodynamics Classification
          Spectroscopy, Near-Infrared
          Human
          Male
          Female
          Adult
          Paradigms
          Funding Source
          Adult: 19-44 years
          Male
          Female
      ab: Functional near-infrared spectroscopy (fNIRS) is an emerging optical technique, which can assess brain activities associated with tasks. In this study, six participants were asked to perform three imageries of hand clenching associated with force and speed, respectively. Joint mutual information (JMI) criterion was used to extract the optimal features of hemodynamic responses. And extreme learning machine (ELM) was employed to be the classifier. ELM solved the major bottleneck of feedforward neural networks in learning speed, this classifier was easily implemented and less sensitive to specified parameters. The 2-class fNIRS-BCI system was firstly built with an average accuracy of 76.7 %, when all force and speed tasks were categorized as one class, respectively. The multi-class systems based on different levels of force and speed attempted to be investigated, the accuracies were moderate. This study provided a novel paradigm for establishing fNIRS-BCI system, and provided a possibility to produce more degrees of freedom in BCI system.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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