28
S.-P. Kim
This instruction would be especially crucial for the studies recruiting younger participants. Sixth, not only highly contaminated channels but also highly contaminated
trials are often eliminated from the analysis. The elimination of contaminated trials
is usually conducted after all the preprocessing steps but its operational principle
is similar to other preprocessing methods. Generally, the trials containing the EEG
signal magnitude greater than a threshold level (e.g. ±150 µV) are classified as being
contaminated [89]. Here, the threshold must be specified depending on experimental conditions. Rejection of too many trials would cause a shortage of the amount
of data in the subsequent analyses, so a careful interactive investigation between
preprocessing methods and trial rejection should be considered. Finally, a developed preprocessing pipeline may call for assessments based on feedbacks from the
designated applications (e.g. classification of the user intention for brain-computer
interfaces). Consequently, it is worth deliberating an end-to-end design of EEG signal
processing, from the recording to the interpretation of EEG as a whole.
References
1. M.T. Akhtar, W. Mitsuhashi, C.J. James, Employing spatially constrained ICA and wavelet
denoising, for automatic removal of artifacts from multichannel EEG data. Sig. Process. 92,
401–416 (2012)
2. L. Albera, A. Kachenoura, P. Comon, A. Karfoul, F. Wendling, L. Senhadji, I. Merlet, ICAbased EEG denoising: a comparative analysis of fifteen methods. Bull. Pol. Ac: Tech. 60,
407–418 (2012)
3. M.N. Anastasiadou, M. Christodoulakis, E.S. Papathanasiou, S.S. Papacostas, G.D. Mitsis, Unsupervised detection and removal of muscle artifacts from scalp EEG recordings
using canonical correlation analysis, Wavelets and random forests. Clin. Neurophysiol. 128,
1755–1769 (2017)
4. H. Ashida, J. Tatsuno, J. Okamoto, E. Maru, Field mapping of EEG by unbiased polynomial
interpolation. Comput. Biomed. Res. 17, 267–276 (1984)
5. B. Babadi, E.N. Brown, A review of multitaper spectral analysis. IEEE Trans. Biomed. Eng.
61, 1555–1564 (2014)
6. F. Babiloni, L. Astolfi, Social neuroscience and hyperscanning techniques: past, present and
future. Neurosci. Biobehav. Rev. 44, 76–93 (2014)
7. A.J. Bell, T.J. Sejnowski, An information-maximization approach to blind separation and
blind deconvolution. Neural Comput. 7, 1129–1159 (1995)
8. N. Bigdely-Shamlo, T. Mullen, C. Kothe, K.-M. Su, K.A. Robbins, The PREP pipeline:
standardized preprocessing for large-scale EEG analysis. Front. Neuroinform. 9, 16 (2015)
9. C. Binnie, R. Cooper, F. Mauguiere, J. Osselton, P. Prior, B. Tedman, EEG, Pediatric neurophysiology, special techniques and applications, in Clinical Neurophysiology, vol. 2, ed. by
R.M.F. Cooper, J.W. Osselton, P.F. Prior, B.M. Tedman (Elsevier, Amsterdam, 2003)
10. G. Buzsáki, X.-J. Wang, Mechanisms of gamma oscillations. Annu. Rev. Neurosci. 35,
203–225 (2012)
11. S. Calcagno, F. La Foresta, M. Versaci, Independent component analysis and discrete wavelet
transform for artifact removal in biomedical signal processing. Am. J. Appl. Sci. 11, 57 (2014)
12. N.P. Castellanos, V.A. Makarov, Recovering EEG brain signals: artifact suppression with
wavelet enhanced independent component analysis. J. Neurosci. Methods 158, 300–312
(2006)
S.-P. Kim
This instruction would be especially crucial for the studies recruiting younger participants. Sixth, not only highly contaminated channels but also highly contaminated
trials are often eliminated from the analysis. The elimination of contaminated trials
is usually conducted after all the preprocessing steps but its operational principle
is similar to other preprocessing methods. Generally, the trials containing the EEG
signal magnitude greater than a threshold level (e.g. ±150 µV) are classified as being
contaminated [89]. Here, the threshold must be specified depending on experimental conditions. Rejection of too many trials would cause a shortage of the amount
of data in the subsequent analyses, so a careful interactive investigation between
preprocessing methods and trial rejection should be considered. Finally, a developed preprocessing pipeline may call for assessments based on feedbacks from the
designated applications (e.g. classification of the user intention for brain-computer
interfaces). Consequently, it is worth deliberating an end-to-end design of EEG signal
processing, from the recording to the interpretation of EEG as a whole.
References
1. M.T. Akhtar, W. Mitsuhashi, C.J. James, Employing spatially constrained ICA and wavelet
denoising, for automatic removal of artifacts from multichannel EEG data. Sig. Process. 92,
401–416 (2012)
2. L. Albera, A. Kachenoura, P. Comon, A. Karfoul, F. Wendling, L. Senhadji, I. Merlet, ICAbased EEG denoising: a comparative analysis of fifteen methods. Bull. Pol. Ac: Tech. 60,
407–418 (2012)
3. M.N. Anastasiadou, M. Christodoulakis, E.S. Papathanasiou, S.S. Papacostas, G.D. Mitsis, Unsupervised detection and removal of muscle artifacts from scalp EEG recordings
using canonical correlation analysis, Wavelets and random forests. Clin. Neurophysiol. 128,
1755–1769 (2017)
4. H. Ashida, J. Tatsuno, J. Okamoto, E. Maru, Field mapping of EEG by unbiased polynomial
interpolation. Comput. Biomed. Res. 17, 267–276 (1984)
5. B. Babadi, E.N. Brown, A review of multitaper spectral analysis. IEEE Trans. Biomed. Eng.
61, 1555–1564 (2014)
6. F. Babiloni, L. Astolfi, Social neuroscience and hyperscanning techniques: past, present and
future. Neurosci. Biobehav. Rev. 44, 76–93 (2014)
7. A.J. Bell, T.J. Sejnowski, An information-maximization approach to blind separation and
blind deconvolution. Neural Comput. 7, 1129–1159 (1995)
8. N. Bigdely-Shamlo, T. Mullen, C. Kothe, K.-M. Su, K.A. Robbins, The PREP pipeline:
standardized preprocessing for large-scale EEG analysis. Front. Neuroinform. 9, 16 (2015)
9. C. Binnie, R. Cooper, F. Mauguiere, J. Osselton, P. Prior, B. Tedman, EEG, Pediatric neurophysiology, special techniques and applications, in Clinical Neurophysiology, vol. 2, ed. by
R.M.F. Cooper, J.W. Osselton, P.F. Prior, B.M. Tedman (Elsevier, Amsterdam, 2003)
10. G. Buzsáki, X.-J. Wang, Mechanisms of gamma oscillations. Annu. Rev. Neurosci. 35,
203–225 (2012)
11. S. Calcagno, F. La Foresta, M. Versaci, Independent component analysis and discrete wavelet
transform for artifact removal in biomedical signal processing. Am. J. Appl. Sci. 11, 57 (2014)
12. N.P. Castellanos, V.A. Makarov, Recovering EEG brain signals: artifact suppression with
wavelet enhanced independent component analysis. J. Neurosci. Methods 158, 300–312
(2006)