TWO-MODULAR SYSTEM FOR PROCESSING EEG DATA USING FACTOR ANALYSIS AND MOORE-PENROSE PSEUDOINVERSION


Abstract

This paper describes the concept of obtaining the so-called. the output signal for the purpose, inter alia, the control processes carried out. To this end, proposed the construction of two-modular system for processing and analysis of electrophysiological data on the composition, which includes factor analysis and pseudoinversion Moore Penrose. In the article the problem of high interference sources of EEG signals, which has a negative impact on the process of obtaining the final output using automation or robotics. This implies also the problem of proper and correct identification of sources in the human brain.


Keywords

two-modular system; Moore-Penrose pseudoinversion; factor analysis; EEG data

Accardo A., Affinito M., Carrozzi M., Bouquet F.: Use of the fractal dimension for the analysis of electroencephalographic time series, Biol. Cybern., vol. 77, 1997, 339-350.

Bakardjian H., Cichocki A., Cincotti F., Mattia D., Babiloni F., Grazia Marciani M., De Vico Fallani F., Miwakeichi F., Yamaguchi Y., Martinez P., Salinari S., Tocci A., Astolfi L.: Estimate of causality between cortical spatial patterns during voluntary movements in normal subjects, International Journal of Bioelectromagnetism 8 (1), II/1–II/18, 2006.

Bielińska E. et al.: Identyfikacja Procesów, Gliwice, Wydawnictwo Politechniki Śląskiej, 1997.

Cheung Y.M., Xu L.: Dual multivariate auto-regressive modeling in state space for temporal signal separation, IEEE T. Syst. Man. Cyb. 33 2003, 386-398.

Cichocki A., Zdunek R., Amari S.: Csiszar's divergences for non-negative matrix factorization: Family of new algorithms. LNCS 3889, Springer, 32-39.

Cichocki A., Zdunek R., Amari, S.: New algorithms for non-negative matrix factorization in applications to blind source separation. In Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP-2006.

Cruces S., Cichocki A., Castedo L.: An iterative inversion approach to blind source separation. IEEE Trans. on Neural Networks, 11 (6), 2000, 1423-1437.

Cruces S.A., Castedo L., Cichocki A.: Robust blind source separation algorithms using cumulants. Neurocomputing, 49, 2002, 87-118.

David O., Friston K.J.: A neural mass model for MEG/EEG: coupling and neuronal dynamics, NeuroImage 20 (3), 2003, 1743-1755.

Dvorak I., Holden A.V.: Mathematical Approaches to Brain Functioning Diagnostics, Manchester Univ. Press, 1991.

Gomez-Herrero G., De Clercq W., Anwar H., Egiazarian K. Kara, Van Hu_e O.S., Van Paesschen W.: Automatic removal of ocular artifacts in the eeg without a reference eog channel, In Proc. NORSIG, Reykjavik, Iceland 2006, 130–133.

Hyvarinen A., Kashunen J., Oja E.: Independent Component Analysis, John Wiley & Sons, Ltd, UK. 2001.

Katsikis V.N., Pappas, D.: Fast computing of the Moore–Penrose inverse matrix, Electronic Journal of Linear Algebra 17(1), 2008, 637-650.

Lagerlund T.D., Sharbrough F.W., Busacker N.E.: Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition, J. Clin. Neurophysiol., vol. 14, 1997, 73-82.

Lee D.D., Seung H. S.: Learning of the parts of objects by non-negative matrix factorization. Nature, 401, 1999, 788-791.

Li Y., Cichocki A., Amari S.: Analysis of sparse representation and blind source separation. Neural Computation, 16 (6), 2004, 1193-1204.

Li Y., Cichocki A., Amari S.: Blind estimation of channel parameters and source components for EEG signals: A sparse factorization approach. IEEE Transactions on Neural Networks, 2006, 17, 419-431.

Li Y., Cichocki A., Amari S., Shishkin S., Cao J., Gu F.: Sparse representation and its applications in blind source separation. In Seventeenth Annual Conference on Neural Information Processing Systems (NIPS-2003). Vancouver.

Lin C.J.: Projected gradient methods for non-negative matrix factorization (Tech. Rep.) Department of Computer Science, National Taiwan University, 2005.

Petralias A., Katsikis V.N., Pappas D.: An improved method for the computation of the Moore–Penrose inverse matrix, Applied Mathematics and Computation 217(23) 2011, 9828-9834.

Paszkiel S.: Augmented reality of technological environment in correlation with brain computer interfaces for control processes, Advances in Intelligent Systems and Computing 267 - AISC, Springer Switzerland 2014, 197-203.

Paszkiel S.: The use of Brain Computer Interfaces in the control processes based on industrial PC in terms of the methods of EEG signal analysis, Journal of Medical Informatics & Technologies - Vol. 22 2013, 55-62.

Paszkiel S., Błachowicz A.: The application of electroencephalographic signals in the aspect of controlling a mobile robot for measurements of incomplete discharges, Przegląd Elektrotechniczny, R. 86 NR 8/2010, 303-306.

Paszkiel S.: The population modeling of neuronal cell fractions for the use of controlling a mobile robot. Pomiary, Automatyka, Robotyka, vol. 2, 2013, 254-259.


Published : 2014-12-09


Paszkiel, S. (2014). TWO-MODULAR SYSTEM FOR PROCESSING EEG DATA USING FACTOR ANALYSIS AND MOORE-PENROSE PSEUDOINVERSION. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 4(4), 62-64. https://doi.org/10.5604/20830157.1130196

Szczepan Paszkiel  s.paszkiel@po.opole.pl
Opole University of Technology  Poland