A combination of transductive and inductive learning for handling non-stationarities in motor imagery classification

@article{Raza2016ACO,
  title={A combination of transductive and inductive learning for handling non-stationarities in motor imagery classification},
  author={Haider Raza and Hubert Cecotti and Girijesh Prasad},
  journal={2016 International Joint Conference on Neural Networks (IJCNN)},
  year={2016},
  pages={763-770}
}
A major issue for bringing brain-computer interface (BCI) based on electroencephalogram (EEG) recordings outside of laboratories is the non-stationarities of EEG signals. Varying statistical properties of the signals during inter- or intra-session transfers can lead to deteriorated BCI performances over time. These variations may cause the input data distribution to shift when transitioning from the training phase (calibration session) to the testing/operating phase resulting in a covariate… CONTINUE READING

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