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Volumn , Issue , 2010, Pages 138-141

A tensorial approach to single trial recognition for brain computer interface

Author keywords

[No Author keywords available]

Indexed keywords

COMMON SPATIAL PATTERNS; DATA SETS; INPUT DATAS; MATRIX REPRESENTATION; MOTOR IMAGERY; MOVEMENT RELATED POTENTIALS; SINGLE TRIAL;

EID: 78751507642     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ATC.2010.5672697     Document Type: Conference Paper
Times cited : (4)

References (13)
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  • 4
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    • "Data set for single trial EEG classification in BCI," Center for Brain-Like Computing and Machine Intelligence. [Online]. Available: http://bcmi.sjtu.edu.cn/data1/
    • Data Set for Single Trial EEG Classification in BCI
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    • Optimal spatial filtering of single trial EEG during imagined hand movement
    • H. Ramoser, J. Mller-Gerking, and G. Pfurtscheller, "Optimal spatial filtering of single trial EEG during imagined hand movement," IEEE Trans. Rehab. Eng, vol. 8, pp. 441-446, 1998.
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  • 6
    • 0034144761 scopus 로고    scopus 로고
    • On the best rank-1 and rank-(R1,R2,...,RN) approximation of higher-order tensors
    • L. De Lathauwer, B. D. Moor, and J. Vandewalle, "On the best rank-1 and rank-(R1,R2,. . . ,RN) approximation of higher-order tensors," SIAM Journal of Matrix Analysis and Applications, vol. 21, no. 4, pp. 1324-1342, 2000.
    • (2000) SIAM Journal of Matrix Analysis and Applications , vol.21 , Issue.4 , pp. 1324-1342
    • De Lathauwer, L.1    Moor, B.D.2    Vandewalle, J.3
  • 7
    • 54949151189 scopus 로고    scopus 로고
    • Fast and efficient algorithms for non-negative Tucker decomposition
    • Proc. of The Fifth International Symposium on Neural Networks, Springer Beijing, China, 24-28, September
    • A.-H. Phan and A. Cichocki, "Fast and efficient algorithms for non-negative Tucker decomposition," in Proc. of The Fifth International Symposium on Neural Networks, Springer LNCS-5264, Beijing, China, 24-28, September 2008, pp. 772-782.
    • (2008) LNCS , vol.5264 , pp. 772-782
    • Phan, A.-H.1    Cichocki, A.2
  • 9
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    • Local learning rules for nonnegative Tucker decomposition
    • A.-H. Phan and A. Cichocki, "Local learning rules for nonnegative Tucker decomposition," ICONIP, 2009.
    • (2009) ICONIP
    • Phan, A.-H.1    Cichocki, A.2
  • 10
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    • Extended HALS algorithm for nonnegative Tucker decomposition and applications for classification and analysis of multidimensional data
    • accepted
    • -, "Extended HALS algorithm for nonnegative Tucker decomposition and applications for classification and analysis of multidimensional data," Neurocomputing (accepted), 2010.
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  • 11
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    • Tensor decompositions and applications
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  • 12
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    • Tensor decompositions for feature extraction and classification of high dimensional datasets
    • IEICE (invited paper)
    • A.-H. Phan and A. Cichocki, "Tensor decompositions for feature extraction and classification of high dimensional datasets," Nonlinear Theory and Its Applications, IEICE (invited paper), 2010.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.