EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine.

@article{Gao2016EEGCF,
  title={EEG classification for motor imagery and resting state in BCI applications using multi-class Adaboost extreme learning machine.},
  author={Lin Gao and Wei Cheng and Jinhua Zhang and Jue Wang},
  journal={The Review of scientific instruments},
  year={2016},
  volume={87 8},
  pages={085110}
}
Brain-computer interface (BCI) systems provide an alternative communication and control approach for people with limited motor function. Therefore, the feature extraction and classification approach should differentiate the relative unusual state of motion intention from a common resting state. In this paper, we sought a novel approach for multi-class classification in BCI applications. We collected electroencephalographic (EEG) signals registered by electrodes placed over the scalp during left… CONTINUE READING

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Shrinkage estimator based common spatial pattern for multi-class motor imagery classification by hybrid classifier

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