Towards an EEG-based brain-computer interface for online robot control


According to New York Times, 5.6 million people in the United States are paralyzed to some degree. Motivated by requirements of these paralyzed patients in controlling assisted-devices that support their mobility, we present a novel EEG-based BCI system, which is composed of an Emotive EPOC neuroheadset, a laptop and a Lego Mindstorms NXT robot in this paper. We provide online learning algorithms that consist of k-means clustering and principal component analysis to classify the signals from the headset into corresponding action commands. Moreover, we also discuss how to integrate the Emotiv EPOC headset into the system, and how to integrate the LEGO robot. Finally, we evaluate the proposed online learning algorithms of our BCI system in terms of precision, recall, and the F-measure, and our results show that the algorithms can accurately classify the subjects’ thoughts into corresponding action commands.

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The work is supported in part by the National Natural Science Foundation of China Grant 61402380, U.S. National Science Foundation Grants CNS-1253506 (CAREER) and CNS-1250180, the Fundamental Research Funds for the Central Universities Grant XDJK2015B030, the State Ethnic Affairs Commission of China Grant 14GZZ012, and the Science and Technology Foundation of Guizhou Grant LH20147386.

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Correspondence to Yantao Li.

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Li, Y., Zhou, G., Graham, D. et al. Towards an EEG-based brain-computer interface for online robot control. Multimed Tools Appl 75, 7999–8017 (2016).

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  • EEG
  • BCI system
  • k-means clustering algorithm
  • Principal component analysis