Normalization effects in matching pursuit algorithm with gabor dictionaries

• Autorzy: Różański P. T.
• Języki publikacji: EN

Abstrakty

EN

The matching pursuit (MP) algorithm is a greedy method for signal decomposition used in video coding, data compression, and, particularly, analysis of EEG signals in various paradigms, including P300 and ER(D)S (motor imagery). An important issue for MP implementation is a correct treatment of normalization of atoms (functions) used in computations. Failing to account for normalization-related effects may affect both the numerical stability and the reliability of the algorithm. This paper describes these normalization effects, evaluates their impact on the algorithm’s performance, and describe the proper approach together with a ready-to-use implementation, available under a General Public Licence (GPL). Several performance optimizations used as a part of this implementation are also described.

Słowa kluczowe

EN

matching pursuit; time-frequency; wavelet; EEG analysis

PL

poszukiwanie dopasowujące; czas-częstotliwość; analiza EEG

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2018
• Tom: Vol. 26, nr 2
• Strony: 187--199
• Opis fizyczny: Bibliogr. 8 poz., wykr.

Twórcy

autor

Różański P. T.

• College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences (MISMaP), University of Warsaw, ul. Stefana Banacha 2C, 02-097 Warszawa

Bibliografia

• [1] Mallat, S. G. and Zhang, Z., Matching pursuits with time-frequency dictionaries, IEEE Transactions on Signal Processing, Vol. 41, No. 12, Dec 1993, pp. 3397-3415.
• [2] Durka, P. J., Ircha, D., Neuper, C., and Pfurtscheller, G., Time-frequency microstructure of event-related electro-encephalogram desynchronisation and synchronisation, Medical and Biological Engineering and Computing, Vol. 39, 2001, pp. 315-321.
• [3] Sieluzycki, C., Konig, R., Matysiak, A., Kus, R., Ircha, D., and Durka, P. J., Single-Trial Evoked Brain Responses Modeled by Multivariate Matching Pursuit, IEEE Transactions on Biomedical Engineering, Vol. 56, No. 1, Jan 2009, pp. 74-82.
• [4] Durka, P. J., Matysiak, A., Montes, E. M., Sosa, P. V., and Blinowska, K. J., Multichannel matching pursuit and EEG inverse solutions. Journal of neuroscience methods, Vol. 148 1, 2005, pp. 49-59.
• [5] Durka, P. J., Malinowska, U., Zieleniewska, M., O’Reilly, C., Różański, P. T., and Żygierewicz, J., Spindles in Svarog: framework and software for parametrization of EEG transients, Front. Hum. Neurosci., 2015.
• [6] Kuś, R., Różański, P. T., and Durka, P. J., Multivariate matching pursuit in optimal Gabor dictionaries: theory and software with interface for EEG/MEG via Svarog, BioMedical Engineering OnLine, Vol. 12, No. 1, Sep 2013, pp. 94.
• [7] Krstulovic, S. and Gribonval, R., MPTK: Matching Pursuit made Tractable, In: Proc. Int. Conf. Acoust. Speech Signal Process. (ICASSP’06), Vol. 3, Toulouse, France, May 2006, pp. III-496 - III-499.
• [8] Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C., Introduction to Algorithms, Third Edition, The MIT Press, 3rd ed., 2009.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).