32
S.-P. Kim
80. P.P. Mitra, B. Pesaran, Analysis of dynamic brain imaging data. Biophys. J. 76, 691–708
(1999)
81. A. Mognon, J. Jovicich, L. Bruzzone, M. Buiatti, ADJUST: an automatic EEG artifact detector
based on the joint use of spatial and temporal features. Psychophysiology 48, 229–240 (2011)
82. F. Morbidi, A. Garulli, D. Prattichizzo, C. Rizzo, P. Manganotti, S. Rossi, Off-line removal
of TMS-induced artifacts on human electroencephalography by Kalman filter. J. Neurosci.
Methods 162, 293–302 (2007)
83. T. Mullen, NITRC: CleanLine: Tool/Resource Info (2012)
84. H. Nolan, R. Whelan, R. Reilly, FASTER: fully automated statistical thresholding for EEG
artifact rejection. J. Neurosci. Methods 192, 152–162 (2010)
85. B. Noureddin, P.D. Lawrence, G.E. Birch, Online removal of eye movement and blink EEG
artifacts using a high-speed eye tracker. IEEE Trans. Biomed. Eng. 59, 2103–2110 (2012)
86. H. Peng, B. Hu, Q. Shi, M. Ratcliffe, Q. Zhao, Y. Qi, G. Gao, Removal of ocular artifacts in
EEG—An improved approach combining DWT and ANC for portable applications. IEEE J.
Biomed. Health Inform. 17, 600–607 (2013)
87. F. Perrin, J. Pernier, O. Bertnard, M. Giard, J. Echallier, Mapping of scalp potentials by surface
spline interpolation. Electroencephalogr. Clin. Neurophysiol. 66, 75–81 (1987)
88. T.T. Pham, R.J. Croft, P.J. Cadusch, R.J. Barry, A test of four EOG correction methods using
an improved validation technique. Int. J. Psychophysiol. 79, 203–210 (2011)
89. T. Picton, S. Bentin, P. Berg, E. Donchin, S. Hillyard, R. Johnson, G. Miller, W. Ritter, D.
Ruchkin, M. Rugg, Guidelines for using human event-related potentials to study cognition:
recording standards and publication criteria. Psychophysiology 37, 127–152 (2000)
90. I. Rejer, P. Górski, Independent Component Analysis for EEG data preprocessing-algorithms
comparison, in Computer Information Systems and Industrial Management, vol. 8104, ed. by
K. Saeed, R. Chaki, A. Cortesi, S. Wierzcho´ n (Springer, Heidelberg, 2013)
91. S. Romero, M. Mañanas, M.J. Barbanoj, Ocular reduction in EEG signals based on adaptive
filtering, regression and blind source separation. Ann. Biomed. Eng. 37, 176–191 (2009)
92. S. Romero, M.A. Mañanas, M.J. Barbanoj, A comparative study of automatic techniques
for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a
simulation case. Comput. Biol. Med. 38, 348–360 (2008)
93. L. Sörnmo, P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications (Academic Press, Elsevier, Amsterdam, 2005)
94. D. Safieddine, A. Kachenoura, L. Albera, G. Birot, A. Karfoul, A. Pasnicu, A. Biraben, F.
Wendling, L. Senhadji, I. Merlet, Removal of muscle artifact from EEG data: comparison
between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches.
EURASIP J. Adv. Signal Process. 2012, 127 (2012)
95. A.J. Shackman, B.W. McMenamin, H.A. Slagter, J.S. Maxwell, L.L. Greischar, R.J. Davidson,
Electromyogenic artifacts and electroencephalographic inferences. Brain Topogr. 22, 7–12
(2009)
96. B. Singh, H. Wagatsuma, A removal of eye movement and blink artifacts from EEG data
using morphological component analysis. Comput. Math. Methods Med. (2017)
97. A.C. Soong, J.C. Lind, G.R. Shaw, Z.J. Koles, Systematic comparisons of interpolation techniques in topographic brain mapping. Clin. Neurophysiol. 87, 185–195 (1993)
98. M. Steriade, Grouping of brain rhythms in corticothalamic systems. Neuroscience 137,
1087–1106 (2006)
99. K.T. Sweeney, S.F. McLoone, T.E. Ward, The use of ensemble empirical mode decomposition
with canonical correlation analysis as a novel artifact removal technique. IEEE Trans. Biomed.
Eng. 60, 97–105 (2013)
100. K.T. Sweeney, T.E. Ward, S.F. McLoone, Artifact removal in physiological signals—practices
and possibilities. IEEE Trans. Inf. Technol. Biomed. 16, 488–500 (2012)
101. A.R. Teixeira, A.M. Tomé, K. Stadlthanner, E.W. Lang, (eds.), Nonlinear projective techniques
to extract artifacts in biomedical signals. Signal Processing Conference, 2006 14th European;
IEEE (2006)
S.-P. Kim
80. P.P. Mitra, B. Pesaran, Analysis of dynamic brain imaging data. Biophys. J. 76, 691–708
(1999)
81. A. Mognon, J. Jovicich, L. Bruzzone, M. Buiatti, ADJUST: an automatic EEG artifact detector
based on the joint use of spatial and temporal features. Psychophysiology 48, 229–240 (2011)
82. F. Morbidi, A. Garulli, D. Prattichizzo, C. Rizzo, P. Manganotti, S. Rossi, Off-line removal
of TMS-induced artifacts on human electroencephalography by Kalman filter. J. Neurosci.
Methods 162, 293–302 (2007)
83. T. Mullen, NITRC: CleanLine: Tool/Resource Info (2012)
84. H. Nolan, R. Whelan, R. Reilly, FASTER: fully automated statistical thresholding for EEG
artifact rejection. J. Neurosci. Methods 192, 152–162 (2010)
85. B. Noureddin, P.D. Lawrence, G.E. Birch, Online removal of eye movement and blink EEG
artifacts using a high-speed eye tracker. IEEE Trans. Biomed. Eng. 59, 2103–2110 (2012)
86. H. Peng, B. Hu, Q. Shi, M. Ratcliffe, Q. Zhao, Y. Qi, G. Gao, Removal of ocular artifacts in
EEG—An improved approach combining DWT and ANC for portable applications. IEEE J.
Biomed. Health Inform. 17, 600–607 (2013)
87. F. Perrin, J. Pernier, O. Bertnard, M. Giard, J. Echallier, Mapping of scalp potentials by surface
spline interpolation. Electroencephalogr. Clin. Neurophysiol. 66, 75–81 (1987)
88. T.T. Pham, R.J. Croft, P.J. Cadusch, R.J. Barry, A test of four EOG correction methods using
an improved validation technique. Int. J. Psychophysiol. 79, 203–210 (2011)
89. T. Picton, S. Bentin, P. Berg, E. Donchin, S. Hillyard, R. Johnson, G. Miller, W. Ritter, D.
Ruchkin, M. Rugg, Guidelines for using human event-related potentials to study cognition:
recording standards and publication criteria. Psychophysiology 37, 127–152 (2000)
90. I. Rejer, P. Górski, Independent Component Analysis for EEG data preprocessing-algorithms
comparison, in Computer Information Systems and Industrial Management, vol. 8104, ed. by
K. Saeed, R. Chaki, A. Cortesi, S. Wierzcho´ n (Springer, Heidelberg, 2013)
91. S. Romero, M. Mañanas, M.J. Barbanoj, Ocular reduction in EEG signals based on adaptive
filtering, regression and blind source separation. Ann. Biomed. Eng. 37, 176–191 (2009)
92. S. Romero, M.A. Mañanas, M.J. Barbanoj, A comparative study of automatic techniques
for ocular artifact reduction in spontaneous EEG signals based on clinical target variables: a
simulation case. Comput. Biol. Med. 38, 348–360 (2008)
93. L. Sörnmo, P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications (Academic Press, Elsevier, Amsterdam, 2005)
94. D. Safieddine, A. Kachenoura, L. Albera, G. Birot, A. Karfoul, A. Pasnicu, A. Biraben, F.
Wendling, L. Senhadji, I. Merlet, Removal of muscle artifact from EEG data: comparison
between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches.
EURASIP J. Adv. Signal Process. 2012, 127 (2012)
95. A.J. Shackman, B.W. McMenamin, H.A. Slagter, J.S. Maxwell, L.L. Greischar, R.J. Davidson,
Electromyogenic artifacts and electroencephalographic inferences. Brain Topogr. 22, 7–12
(2009)
96. B. Singh, H. Wagatsuma, A removal of eye movement and blink artifacts from EEG data
using morphological component analysis. Comput. Math. Methods Med. (2017)
97. A.C. Soong, J.C. Lind, G.R. Shaw, Z.J. Koles, Systematic comparisons of interpolation techniques in topographic brain mapping. Clin. Neurophysiol. 87, 185–195 (1993)
98. M. Steriade, Grouping of brain rhythms in corticothalamic systems. Neuroscience 137,
1087–1106 (2006)
99. K.T. Sweeney, S.F. McLoone, T.E. Ward, The use of ensemble empirical mode decomposition
with canonical correlation analysis as a novel artifact removal technique. IEEE Trans. Biomed.
Eng. 60, 97–105 (2013)
100. K.T. Sweeney, T.E. Ward, S.F. McLoone, Artifact removal in physiological signals—practices
and possibilities. IEEE Trans. Inf. Technol. Biomed. 16, 488–500 (2012)
101. A.R. Teixeira, A.M. Tomé, K. Stadlthanner, E.W. Lang, (eds.), Nonlinear projective techniques
to extract artifacts in biomedical signals. Signal Processing Conference, 2006 14th European;
IEEE (2006)