Connectivity pattern modeling of motor imagery EEG

@article{Li2013ConnectivityPM,
  title={Connectivity pattern modeling of motor imagery EEG},
  author={Xinyang Li and Sim Heng Ong and Yaozhang Pan and Kai Keng Ang},
  journal={2013 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB)},
  year={2013},
  pages={94-100}
}
In this paper, the functional connectivity network of motor imagery based on EEG is investigated to understand brain function during motor imagery. In particular, partial directed coherence and directed transfer function measurements are applied to multi-channel EEG data to find out event related connectivity pattern with the direction and strength. The t-test is applied to these connectivity measurements to compare the network between motor imagery and the rest state. The possible relationship… CONTINUE READING

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Key Quantitative Results

  • It is revealed from the comparison that the proposed MVAR feature extraction method improves the performance of the classifier, with the average classification accuracy of 64.97% higher than that of CSP (63.22%).

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