Biomedical Engineering Reference
In-Depth Information
[17]
Debener, S., et al., “Improved Quality of Auditory Event-Related Potentials Recorded
Simultaneously with 3-T fMRI: Removal of the Ballistocardiogram Artifact,”
NeuroImage
,
Vol. 34, No. 2, 2007, pp. 587-597.
[18]
Delorme, A., et al., “Enhanced Detection of Artifacts in EEG Data Using Higher-Order Sta-
tistics and Independent Component Analysis,”
NeuroImage,
Vol. 34, No. 4, 2007,
pp. 1443-1449.
[19]
Overton, D. A., and C. Shagass, “Distribution of Eye Movement and Eyeblink Potentials
over the Scalp,”
Electroencephalogr. Clin. Neurophysiol.,
Vol. 27, No. 5, 1969, p. 546.
[20]
Barry, W., and G. M. Jones, “Influence of Eye Lid Movement upon Electro-Oculographic
Recording of Vertical Eye Movements,”
Aerosp. Med.,
Vol. 36, 1965, pp. 855-858.
[21]
Croft, R. J., et al., “EOG Correction: A Comparison of Four Methods,”
Psychophysiology
,
Vol. 42, No. 1, 2005, pp. 16-24.
[22]
Gratton, G.,
et
al.,
“A New
Method for Off-Line
Removal
of Ocular
Artifact,”
Electroencephalogr. Clin. Neurophysiol.,
Vol. 55, No. 4, 1983, pp. 468-484.
[23]
Semlitsch, H. V., et al., “A Solution for Reliable and Valid Reduction of Ocular Artifacts,
Applied to the P300 ERP,”
Psychophysiology,
Vol. 23, No. 6, 1986, pp. 695-703.
[24]
Makeig, S., et al., “Independent Component Analysis of Electroencephalographic Data,” in
Advances in Neural Information Processing Systems,
D. Touretzky, M. Mozer, and M.
Hasselmo, (eds.), Vol. 8, 1996, Cambridge, MA: MIT Press, pp. 145-151.
[25]
Cardoso, J. F., and B. H. Laheld, “Equivariant Adaptive Source Separation,”
IEEE Trans.
on Signal Processing,
Vol. 44, 1996, pp. 3017-3030.
[26]
Herault, J., and C. Jutten, “Space or Time Adaptive Signal Processing by Neural Network
Models,”
Proc. AIP Conf. on Neural Networks for Computing,
1986, pp. 206-211.
[27]
Jutten, C., and J. Herault, “Blind Separation of Sources I. An Adaptive Algorithm Based on
Neuromimetic Architecture,”
Signal Processing,
Vol. 24, 1991, pp. 1-10.
[28]
Pham, D. T., P. Garat, and C. Jutten, “Separation of a Mixture of Independent Sources
Through a Maximum Likelihood Approach,”
Proc. EUSIPCO,
1992, pp. 771-774.
[29]
Comon, P., “Independent Component Analysis, A New Concept?”
Signal Processing,
Vol. 36, 1994, pp. 287-314.
[30]
Cichocki, A., R. Unbehauen, and E. Rummert, “Robust Learning Algorithm for Blind Sepa-
ration of Signals,”
Electronics Letters,
Vol. 30, 1994, pp. 1386-1387.
[31]
Bell, A. J., and T. J. Sejnowski, “An Information-Maximization Approach to Blind Separa-
tion and Blind Deconvolution,”
Neural Computation,
Vol. 7, 1995, pp. 1129-1159.
[32]
Amari, S., “Natural Gradient Works Efficiently in Learning,”
Neural Computation,
Vol.
10, 1998, pp. 251-276.
[33]
Girolami, M., “An Alternative Perspective on Adaptive Independent Component Analysis
Algorithm,”
Neural Computation,
Vol. 10, 1998, pp. 2103-2114.
[34]
Lee, T. W., M. Girolami, and T. J. Sejnowski, “Independent Component Analysis Using an
Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources,”
Neu-
ral Computation,
Vol. 11, 1999, pp. 417-441.
[35]
Nadal, J. P., and N. Parga, “Non-Linear Neurons in the Low Noise Limit: A Factorial Code
Maximises Information Transfer,”
Network,
Vol. 5, 1994, pp. 565-581.
[36]
Pearlmutter, B., and L. Parra, “Maximum Likelihood Blind Source Separation: A Con-
text-Sensitive Generalization of ICA,” in
Advances in Neural Information Processing Sys-
tems,
D. Touretzky, M. Mozer, and M. Hasselmo, (eds.), Vol. 9, 1997, Cambridge, MA:
MIT Press, pp. 613-619.
[37]
Pham, D. T., “Blind Separation of Instantaneous Mixture of Sources Via an Independent
Component Analysis,”
IEEE Trans. on Signal Processing,
Vol. 44, No. 11, 1996,
pp. 2768-2779.
[38]
Hyvärinen, A., and E. Oja, “A Fast Fixed-Point Algorithm for Independent Component
Analysis,”
Neural Computation,
Vol. 9, No. 7, 1997, pp. 1483-1492.
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