A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data

@article{Chiarelli2015AKW,
  title={A kurtosis-based wavelet algorithm for motion artifact correction of fNIRS data},
  author={Antonio Maria Chiarelli and Edward L. Maclin and Monica Fabiani and Gabriele Gratton},
  journal={NeuroImage},
  year={2015},
  volume={112},
  pages={128-137}
}
Movements are a major source of artifacts in functional Near-Infrared Spectroscopy (fNIRS). Several algorithms have been developed for motion artifact correction of fNIRS data, including Principal Component Analysis (PCA), targeted Principal Component Analysis (tPCA), Spline Interpolation (SI), and Wavelet Filtering (WF). WF is based on removing wavelets with coefficients deemed to be outliers based on their standardized scores, and it has proven to be effective on both synthetized and real… CONTINUE READING
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