Backpropagation neural networks training for single trial EEG classification

  title={Backpropagation neural networks training for single trial EEG classification},
  author={Arjon Turnip and Keum-shik Hong and Shuzhi Sam Ge},
  journal={Proceedings of the 29th Chinese Control Conference},
EEG recordings provide an important means of brain-computer communication, but their classification accuracy is limited by unforeseeable signal variations due to artifacts or recognizer-subject feedback. A number of techniques recently have been developed to address the related problem of recognizer robustness to uncontrollable signal variation. In this paper, we propose a classification method entailing time-series EEG signals with backpropagation neural networks (BPNN). To test the… CONTINUE READING