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.