2 Preprocessing of EEG
29
13. W.-D. Chang, H.-S. Cha, K. Kim, C.-H. Im, Detection of eye blink artifacts from single
prefrontal channel electroencephalogram. Comput. Methods Programs Biomed. 124, 19–30
(2016)
14. M. Chaumon, D.V. Bishop, N.A. Busch, A practical guide to the selection of independent
components of the electroencephalogram for artifact correction. J. Neurosci. Methods 250,
47–63 (2015)
15. N. Chauveau, J. Morucci, X. Franceries, P. Celsis, B. Rigaud, Resistor mesh model of a
spherical head: Part 1: Applications to scalp potential interpolation. Med. Biol. Eng. Comput.
43, 694–702 (2005)
16. X. Chen, C. He, H. Peng, Removal of muscle artifacts from single-channel EEG based on
ensemble empirical mode decomposition and multiset canonical correlation analysis. J. Appl.
Math. 2014 (2014)
17. M.E. Chowdhury, K.J. Mullinger, P. Glover, R. Bowtell, Reference layer artefact subtraction
(RLAS): a novel method of minimizing EEG artefacts during simultaneous fMRI. NeuroImage
84, 307–319 (2014)
18. J.L. Cole, G. Goldberg, Central nervous system electrophysiology, in Physical Medicine &
Rehabilitation: Principles and Practice, 4th edn., ed. by J.A. DeLisa, B.M. Gans, N.E. Walsh
(Lippincott Williams & Wilkins, Philadelphia, 2005)
19. P. Comon, Independent component analysis, a new concept? Sig. Process. 36, 287–314 (1994)
20. M. Congedo, C. Gouy-Pailler, C. Jutten, On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics. Clin. Neurophysiol. 119, 2677–2686 (2008)
21. R.J. Croft, R.J. Barry, EOG correction: a new aligned-artifact average solution. Clin. Neurophysiol. 107, 395–401 (1998)
22. R.J. Croft, R.J. Barry, EOG correction of blinks with saccade coefficients: a test and revision
of the aligned-artefact average solution. Clin. Neurophysiol. 111, 444–451 (2000)
23. R.J. Croft, R.J. Barry, Removal of ocular artifact from the EEG: a review. Neurophysiol. Clin.
30, 5–19 (2000)
24. I. Daly, M. Billinger, R. Scherer, G. Müller-Putz, On the automated removal of artifacts related
to head movement from the EEG. IEEE Trans. Neural. Syst. Rehab. Eng. 21, 427–434 (2013)
25. I. Daly, N. Nicolaou, S.J. Nasuto, K. Warwick, Automated artifact removal from the electroencephalogram: a comparative study. Clin. EEG Neurosci. 44, 291–306 (2013)
26. I. Daly, R. Scherer, M. Billinger, G. Müller-Putz, FORCe: fully online and automated artifact
removal for brain-computer interfacing. IEEE Trans. Neural. Syst. Rehab. Eng. 23, 725–736
(2015)
27. A. de Cheveigné, Sparse time artifact removal. J. Neurosci. Methods 262, 14–20 (2016)
28. A. de Cheveigné, L.C. Parra, Joint decorrelation, a versatile tool for multichannel data analysis.
NeuroImage 98, 487–505 (2014)
29. W. De Clercq, A. Vergult, B. Vanrumste, W. Van Paesschen, S. Van Huffel, Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans.
Biomed. Eng. 53, 2583–2587 (2006)
30. M. De Vos, W. Deburchgraeve, P. Cherian, V. Matic, R. Swarte, P. Govaert, G.H. Visser, S.
Van Huffel, Automated artifact removal as preprocessing refines neonatal seizure detection.
Clin. Neurophysiol. 122, 2345–2354 (2011)
31. A. Delorme, S. Makeig, EEGLAB: an open source toolbox for analysis of single-trial EEG
dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004)
32. A. Delorme, T. Sejnowski, S. Makeig, Enhanced detection of artifacts in EEG data using
higher-order statistics and independent component analysis. NeuroImage 34, 1443–1449
(2007)
33. A. Doering, H. Jäger, H. Witte, M. Galicki, C. Schelenz, M. Specht, K. Reinhart, M. Eiselt,
Adaptable preprocessing units and neural classification for the segmentation of EEG signals.
Meth. Inform. Med. 38, 214–224 (1999)
34. D.L. Donoho, I.M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage. J.
Am. Stat. Assoc. 90, 1200–1224 (1995)
29
13. W.-D. Chang, H.-S. Cha, K. Kim, C.-H. Im, Detection of eye blink artifacts from single
prefrontal channel electroencephalogram. Comput. Methods Programs Biomed. 124, 19–30
(2016)
14. M. Chaumon, D.V. Bishop, N.A. Busch, A practical guide to the selection of independent
components of the electroencephalogram for artifact correction. J. Neurosci. Methods 250,
47–63 (2015)
15. N. Chauveau, J. Morucci, X. Franceries, P. Celsis, B. Rigaud, Resistor mesh model of a
spherical head: Part 1: Applications to scalp potential interpolation. Med. Biol. Eng. Comput.
43, 694–702 (2005)
16. X. Chen, C. He, H. Peng, Removal of muscle artifacts from single-channel EEG based on
ensemble empirical mode decomposition and multiset canonical correlation analysis. J. Appl.
Math. 2014 (2014)
17. M.E. Chowdhury, K.J. Mullinger, P. Glover, R. Bowtell, Reference layer artefact subtraction
(RLAS): a novel method of minimizing EEG artefacts during simultaneous fMRI. NeuroImage
84, 307–319 (2014)
18. J.L. Cole, G. Goldberg, Central nervous system electrophysiology, in Physical Medicine &
Rehabilitation: Principles and Practice, 4th edn., ed. by J.A. DeLisa, B.M. Gans, N.E. Walsh
(Lippincott Williams & Wilkins, Philadelphia, 2005)
19. P. Comon, Independent component analysis, a new concept? Sig. Process. 36, 287–314 (1994)
20. M. Congedo, C. Gouy-Pailler, C. Jutten, On the blind source separation of human electroencephalogram by approximate joint diagonalization of second order statistics. Clin. Neurophysiol. 119, 2677–2686 (2008)
21. R.J. Croft, R.J. Barry, EOG correction: a new aligned-artifact average solution. Clin. Neurophysiol. 107, 395–401 (1998)
22. R.J. Croft, R.J. Barry, EOG correction of blinks with saccade coefficients: a test and revision
of the aligned-artefact average solution. Clin. Neurophysiol. 111, 444–451 (2000)
23. R.J. Croft, R.J. Barry, Removal of ocular artifact from the EEG: a review. Neurophysiol. Clin.
30, 5–19 (2000)
24. I. Daly, M. Billinger, R. Scherer, G. Müller-Putz, On the automated removal of artifacts related
to head movement from the EEG. IEEE Trans. Neural. Syst. Rehab. Eng. 21, 427–434 (2013)
25. I. Daly, N. Nicolaou, S.J. Nasuto, K. Warwick, Automated artifact removal from the electroencephalogram: a comparative study. Clin. EEG Neurosci. 44, 291–306 (2013)
26. I. Daly, R. Scherer, M. Billinger, G. Müller-Putz, FORCe: fully online and automated artifact
removal for brain-computer interfacing. IEEE Trans. Neural. Syst. Rehab. Eng. 23, 725–736
(2015)
27. A. de Cheveigné, Sparse time artifact removal. J. Neurosci. Methods 262, 14–20 (2016)
28. A. de Cheveigné, L.C. Parra, Joint decorrelation, a versatile tool for multichannel data analysis.
NeuroImage 98, 487–505 (2014)
29. W. De Clercq, A. Vergult, B. Vanrumste, W. Van Paesschen, S. Van Huffel, Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans.
Biomed. Eng. 53, 2583–2587 (2006)
30. M. De Vos, W. Deburchgraeve, P. Cherian, V. Matic, R. Swarte, P. Govaert, G.H. Visser, S.
Van Huffel, Automated artifact removal as preprocessing refines neonatal seizure detection.
Clin. Neurophysiol. 122, 2345–2354 (2011)
31. A. Delorme, S. Makeig, EEGLAB: an open source toolbox for analysis of single-trial EEG
dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004)
32. A. Delorme, T. Sejnowski, S. Makeig, Enhanced detection of artifacts in EEG data using
higher-order statistics and independent component analysis. NeuroImage 34, 1443–1449
(2007)
33. A. Doering, H. Jäger, H. Witte, M. Galicki, C. Schelenz, M. Specht, K. Reinhart, M. Eiselt,
Adaptable preprocessing units and neural classification for the segmentation of EEG signals.
Meth. Inform. Med. 38, 214–224 (1999)
34. D.L. Donoho, I.M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage. J.
Am. Stat. Assoc. 90, 1200–1224 (1995)