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

  • Systems-Level Quality Improvement
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Abstract

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.

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Acknowledgments

This work is supported by National Natural Science Foundation (NNSF) of China under Grant 61203368, 81470084 and 61463024 and General Reserve Department of PLA under Grant 9140A26060214ZK63424.

Author information

Authors and Affiliations

  1. State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang, 110016, Peoples Republic of China

    Xuxian Yin, Baolei Xu, Zhidong Wang, Hongyi Li & Gang Shi

  2. University of Chinese Academy of Sciences, Beijing, 100049, Peoples Republic of China

    Xuxian Yin

  3. Key Laboratory of Motor and Brain imaging, Capital Institute of Physical Education, Beijing, 100088, China

    Changhao Jiang

  4. Department of Advanced Robotics, Chiba Institute of Technology, Chiba, 2750016, Japan

    Zhidong Wang

  5. School of Mechanical Engineering & Automation, Northeastern University, Shenyang, China

    Hongyi Li

  6. School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming, 650500, Peoples Republic of China

    Yunfa Fu

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  1. Xuxian Yin
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  2. Baolei Xu
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Correspondence to Baolei Xu.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Yin, X., Xu, B., Jiang, C. et al. Classification of Hemodynamic Responses Associated With Force and Speed Imagery for a Brain-Computer Interface. J Med Syst 39, 53 (2015). https://doi.org/10.1007/s10916-015-0236-0

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