The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
TDSEP — an efficient algorithm for blind separation using time structure
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Citations
Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Separation of a mixture of independent signals using time delayed correlations
Molgedey, L., & Schuster, H. G. (1994) · Physical Review Letters · doi:10.1103/physrevlett.72.3634 · Cited by 882
Cited by
Kraskov, A., Stögbauer, H., & Grassberger, P. (2004) · Physical Review E · doi:10.1103/physreve.69.066138 · Cited by 3,512
EEG artifact removal—state-of-the-art and guidelines
Urigüen, J. A., & Garcia-Zapirain, B. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/3/031001 · Cited by 963
Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram
Clercq, W. D., Vergult, A., Vanrumste, B., Van Paesschen, W., & Van Huffel, S. (2006) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2006.879459 · Cited by 522
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Wehmeyer, C., & Noé, F. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5011399 · Cited by 401
Metadata
- Authors
- Ziehe, A., & Müller, K.-R.
- Venue
- Perspectives in Neural Computing
- Publisher
- Springer London
- Published
- 1998
- Pages
- 675-680
- ISSN
- 1431-6854 (ISSN-L)
- OpenAlex
- W1524841735
- Cited by
- 334
- References
- 3
- FWCI
- 10.23
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: TDSEP — an efficient algorithm for blind separation using time structure. https://app.cochat.ai/papers/10.1007/978-1-4471-1599-1_103
- The paper
- Ziehe, A., & Müller, K.-R. (1998). TDSEP — an efficient algorithm for blind separation using time structure. Perspectives in Neural Computing. https://doi.org/10.1007/978-1-4471-1599-1_103
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.