Blind source separation and sparse bump modelling of time frequency representation of EEG signals: New tools for early detection of Alzheimer's disease

2005 IEEE Workshop on Machine Learning for Signal Processing

Volumn , Issue , 2005, Pages 27-32

  Vialatte, François ^a   Cichocki, Andrzej ^b   Dreyfus, Gerard ^a   Musha, Toshimitsu ^c   Rutkowski, Tomasz M ^b   Gervais, Rémi ^d  

^a CNRS   (France)

^b RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

^c Brain Functions Laboratory Inc   (Japan)

^d INSTITUT DES SCIENCES COGNITIVES   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BLIND SOURCE SEPARATION; DISEASES; LEARNING SYSTEMS; MEDICAL APPLICATIONS; PATIENT MONITORING; REGRESSION ANALYSIS;

ALZHEIMER'S DISEASE; MILD COGNITIVE IMPAIRMENT (MCI); TIME FREQUENCY REPRESENTATION (TFR);

ELECTROENCEPHALOGRAPHY;

EID: 33749065365     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/MLSP.2005.1532869     Document Type: Conference Paper

References (13)

1. T. Musha, T. Asada, F. Yamashita, T. Kinoshita, Z. Chen, H. Matsuda, U. Masatake, and W.R. Shankle, "A new EEG method for estimating cortical neuronal impairment that is sensitive to early stage alzheimers disease," Clinical Neurophysiology, vol. 113, no. 7, pp. 1052-1058, 2002.
2. J. Jeong, "EEG dynamics in patients with alzheimers disease," Clinical Neurophysiology, vol. 115, pp. 1490-1505, 2004.
3. A. Cichocki, S.L. Shishkin, T. Muash, Z. Leonowicz, T. Asada, and T. Kurachi, "EEG filtering based on blind source separation (BSS) for early detection of alzheimers disease," Clinical Neurophysiology, vol. 116, no. 3, pp. 729-737, March 2005.
4. A. Cichocki, "Blind signal processing methods for analyzing multichannel brain signals," International Journal of Bioelectromagnetism, vol. 6, no. 1, 2004.
5. A. Cichocki and S. Amari, Adaptative blind signal and image processing: learning algorithms and applications, Wiley, New York, NY, 2003.
6. A. Cichocki, S. Amari, K. Siwek, and T. Tanaka et al., "ICALAB toolboxes," available online at http://www.bsp.brain.riken.jp/ICALAB/.
7. F. Vialatte, C. Martin, R. Dubois, B. Quenet, R. Gervais, and G. Dreyfus, "A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics," Neural Networks, (submitted).
8. C. Tallon-Baudry, O. Bertrand, C. Delpuech, and J. Pernier, "Stimulus specificity of phase-locked and non-phaselocked 40Hz visual responses in human," Journal of Neuroscience, vol. 16, pp. 4240-4249, 1996.
9. R. Dubois, B. Quenet, Y. Faisandier, and G. Dreyfus, "Building meaningful representations in knowledge-driven nonlinear modeling," Neurocomputing, p. (in print), 2005.
10. th Annual General Meeting of the European Brain and Behaviour Society, Barcelona, Spain, 17-20 September 2003, vol. 63 of Acta Neurobiologiae Experimentalis, supplement.
11. S. Chen, S.A. Billings, and W. Luo, "Orthogonal least squares methods and their application to non-linear system identification," International Journal of Control, vol. 50, pp. 1873-1896, 1989.
12. H. Stoppiglia, G. Dreyfus, R. Dubois, and Y. Oussar, "Ranking a random feature for variable and feature selection," Journal of Machine Learning Research, vol. 3, pp. 1399-1414, 2003.
13. S. Haykin, Neural Networks a Comprehensive Foundation, Prentice Hall, second edition, 1999.