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Review

  • Jiang, X., Bian, G. B., & Tian, Z. (2019). Removal of artifacts from EEG signals: a review. Sensors, 19(5), 987.
  • Islam, M. K., Rastegarnia, A., & Yang, Z. (2016). Methods for artifact detection and removal from scalp EEG: A review. Neurophysiologie Clinique/Clinical Neurophysiology, 46(4-5), 287-305.
  • Bigdely-Shamlo, N., Touryan, J., Ojeda, A., Kothe, C., Mullen, T., & Robbins, K. (2020). Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies. NeuroImage, 207, 116361.
  • Bigdely-Shamlo, N., Touryan, J., Ojeda, A., Kothe, C., Mullen, T., & Robbins, K. (2020). Automated EEG mega-analysis II: Cognitive aspects of event related features. NeuroImage, 207, 116054.

Filtering

  • Resample (scipy.signal.resample_poly)
  • Zero-median time re-reference
  • FIR highpass filter
  • Teplan, M. (2002). Fundamentals of EEG measurement. Measurement science review, 2(2), 1-11.
  • Winkler, I., Debener, S., Müller, K. R., & Tangermann, M. (2015, August). On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP. In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 4101-4105). IEEE.
  • FIR notch filter (line noise removal)
  • FIR lowpass filter
  • Fix filter lags

Artifact Subspace Removal (ASR)

  • Chang, C. Y., Hsu, S. H., Pion-Tonachini, L., & Jung, T. P. (2018, July). Evaluation of artifact subspace reconstruction for automatic EEG artifact removal. In 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1242-1245). IEEE.
  • Blum, S., Jacobsen, N. S., Bleichner, M. G., & Debener, S. (2019). A Riemannian modification of artifact subspace reconstruction for EEG artifact handling. Frontiers in human neuroscience, 13, 141.

Bad Channel Removal

  • RANSAC
  • Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381-395.
  • Jas, M., Engemann, D. A., Bekhti, Y., Raimondo, F., & Gramfort, A. (2017). Autoreject: Automated artifact rejection for MEG and EEG data. NeuroImage, 159, 417-429.
  • Kothe, C. A., & Makeig, S. (2013). BCILAB: a platform for brain–computer interface development. Journal of neural engineering, 10(5), 056014.
  • Re-interpolate missing channels
  • Perrin, F., Pernier, J., Bertnard, O., Giard, M. H., & Echallier, J. F. (1987). Mapping of scalp potentials by surface spline interpolation. Electroencephalography and clinical neurophysiology, 66(1), 75-81.
  • Perrin, F., Pernier, J., Bertrand, O., & Echallier, J. F. (1989). Spherical splines for scalp potential and current density mapping. Electroencephalography and clinical neurophysiology, 72(2), 184-187.
  • Common average re-reference
  • Ludwig, K. A., Miriani, R. M., Langhals, N. B., Joseph, M. D., Anderson, D. J., & Kipke, D. R. (2009). Using a common average reference to improve cortical neuron recordings from microelectrode arrays. Journal of neurophysiology, 101(3), 1679-1689.

PREP

  • Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K. M., & Robbins, K. A. (2015). The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Frontiers in neuroinformatics, 9, 16.