| DC Field | Value | Language |
|---|
| dc.contributor.author | Aizenberg, Igor | |
| dc.contributor.author | Khaliq, Zain | |
| dc.coverage.temporal | 21-25 August 2018, Lviv | |
| dc.date.accessioned | 2020-06-19T12:05:49Z | - |
| dc.date.available | 2020-06-19T12:05:49Z | - |
| dc.date.created | 2018-02-28 | |
| dc.date.issued | 2018-02-28 | |
| dc.identifier.citation | Aizenberg I. Analysis of EEG using Multilayer Neural Network with Multi-Valued Neurons / Igor Aizenberg, Zain Khaliq // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 392–396. — (Hybrid Systems of Computational Intelligence). | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/52530 | - |
| dc.description.abstract | There is a wealth of analysis techniques that can
be used in analyzing data of such a nature as EEG
(Electroencephalogram), yet there are still many more ways
and possibilities of analysis techniques to consider in order to
produce a method that far exceeds the capabilities of the
prevalent method. Since a multilayer neural network with
multi-valued neurons (MLMVN) was successfully used earlier
to decode EEG signals in a brain/computer interface (BCI) by
analysis of their Fourier transform, it seemed very attractive to
use it as a tool for EEG analysis. This work aims to further
investigate how a complex-valued machine learning tool can be
used to analyze EEG in the frequency domain. Our goal was to
check how Fourier transform and complex wavelet transform
of EEG can be analyzed using MLMVN in order to diagnose
epilepsy, its remission or absence. We worked with a
commonly used benchmark data set of epilepsy-related EEGs.
The analysis of the transformed data was performed to
determine a set of relevant statistical characteristics of
DTCWT and Fourier transform components, which were then
used as inputs of the MLMVN. The obtained results show a
very high efficiency of the proposed approach. | |
| dc.format.extent | 392-396 | |
| dc.language.iso | en | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 | |
| dc.relation.uri | http://www.uniklu.ac.at/tewi/downloads/masterthesis_TrampitschStefan_Final_Version.pdf | |
| dc.subject | Complex-Valued Neural Networks | |
| dc.subject | Multi-Valued Neuron | |
| dc.subject | Multilayer Neural Network with Multi-Valued Neurons | |
| dc.subject | MLMVN | |
| dc.subject | EEG | |
| dc.subject | Fourier transform | |
| dc.title | Analysis of EEG using Multilayer Neural Network with Multi-Valued Neurons | |
| dc.type | Conference Abstract | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2018 | |
| dc.contributor.affiliation | Manhattan College Riverdale | |
| dc.format.pages | 5 | |
| dc.identifier.citationen | Aizenberg I. Analysis of EEG using Multilayer Neural Network with Multi-Valued Neurons / Igor Aizenberg, Zain Khaliq // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 392–396. — (Hybrid Systems of Computational Intelligence). | |
| dc.relation.references | [1] A. Hirose, Complex-Valued Neural Networks, 2nd Edn.. Springer, Berlin, Heidelberg, 2012. | |
| dc.relation.references | [2] D. Mandic and V. Su Lee Goh, Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models. John Wiley & Sons, 2009. | |
| dc.relation.references | [3] I. Aizenberg, I., Complex-Valued Neural Networks with MultiValued Neurons. Berlin: Springer-Verlag Publishers, 2011. | |
| dc.relation.references | [4] Y.Nakano, and A.Hirose, “Improvement of Plastic Landmine Visualization Performance by use of ring-CSOM and FrequencyDomain Local Correlation,” IEICE Transactions on Electronics, vol. E92-C, iss. 1, pp. 102-108, Jan. 2009. | |
| dc.relation.references | [5] S. L. Goh, M. Chen, D. H. Popovic, K. Aihara, D. Obradovic and D. P. Mandic, "Complex Valued Forecasting of Wind Profile,"Renewable Energy, vol. 31 , pp. 1733-1750, Sep. 2006. | |
| dc.relation.references | [6] A. Handayani, A.B.Suksmono, T.L.R.Mengko, and A.Hirose, “Blood Vessel Segmentation in Complex-Valued Magnetic Resonance Images with Snake Active Contour Model,” International Journal of E-Health and Medical Communications, vol. 1, iss. 1, pp. 41-52, Jan. 2010. | |
| dc.relation.references | [7] I. Aizenberg, L. Sheremetov, L. Villa-Vargas, and J. MartinezMuñoz, "Multilayer Neural Network with Multi-Valued Neurons in Time Series Forecasting of Oil Production," Neurcomputing, vol. 175, part B, pp. 980-989, Jan. 2016. | |
| dc.relation.references | [8] N.V.Manyakov, I. Aizenberg, N. Chumerin, and M. Van Hulle, “Phase-Coded Brain-Computer Interface Based on MLMVN”, book chapter in Complex-Valued Neural Networks: Advances and Applications (A. Hirose – Ed.), Wiley, 2012, pp. 185-208. | |
| dc.relation.references | [9] I. Aizenberg, C. Moraga, and D. Paliy, "A Feedforward Neural Network based on Multi-Valued Neurons", In Computational Intelligence, Theory and Applications. Advances in Soft Computing , XIV, (B. Reusch - Ed.), Springer, Berlin, Heidelberg, New York, pp. 599-612, 2005. | |
| dc.relation.references | [10] I. Aizenberg, and C. Moraga, “Multilayer Feedforward Neural Network based on Multi-Valued Neurons (MLMVN) and a backpropagation learning algorithm,” Soft Computing, 11, iss. 2, pp. 169-183, Jan. 2007. | |
| dc.relation.references | [11] N. N. Aizenberg, Yu. L. Ivaskiv, D. A. Pospelov, and G.F. Hudiakov, "Multivalued Threshold Functions. Synthesis of Multivalued Threshold Elements," Cybernetics and Systems Analysis, vol. 9, no. 1, pp. 61-77, January 1973. | |
| dc.relation.references | [12] N.N. Aizenberg and I.N. Aizenberg, "CNN Based on Multi-Valued Neuron as a Model of Associative Memory for Gray-Scale Images," Proceedings of the Second IEEE International Workshop on Cellular Neural Networks and their Applications, Munich, pp.36-41, October 14-16, 1992. | |
| dc.relation.references | [13] I. Aizenberg, D. Paliy, J. Zurada, and J. Astola, "Blur Identification by Multilayer Neural Network based on Multi-Valued Neurons", IEEE Transactions on Neural Networks, vol. 19, no. 5, pp. 883-898, May 2008. | |
| dc.relation.references | [14] O. Fink, E. Zio, and U. Weidmann, “Predicting Component Reliability and Level of Degradation with Complex-Valued Neural Networks,” Reliability Engineering & System Safety, vol. 121, pp. 198–206, 2014. | |
| dc.relation.references | [15] I. Aizenberg, A. Luchetta and S. Manetti, “A modified learning algorithm for the multilayer neural network with multi-valued neurons based on the complex QR decomposition,” Soft Computing, vol. 16, iss. 4, pp. 563-575, Apr. 2012. | |
| dc.relation.references | [16] I. Aizenberg, “MLMVN with Soft Margins Learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 9, pp. 1632-1644, September 2014. | |
| dc.relation.references | [17] I. Aizenberg, A. Luchetta, S. Manetti., and C. Piccirilli., “System Identification using FRA and a modified MLMVN with Arbitrary Complex-Valued Inputs,” Proceedings of the 2016 IEEE International Joint Conference on Neural Networks (IJCNN 2016), Vancouver, pp. 4404-4411, July, 2016. | |
| dc.relation.references | [18] E. Aizenberg and I. Aizenberg, “Batch LLS-based Learning Algorithm for MLMVN with Soft Margins,” IEEE Symposium Series of Computational Intelligence (SSCI-2014), pp. 48-55, December, 2014. | |
| dc.relation.references | [19] R. Caton, "The electric currents of brain", British Medical Journal, vol. 2, pp. 278, 1875. | |
| dc.relation.references | [20] H. Berger, "Über das Elektroenkephalogramm des Menschen", Arch Psychiatr Nervenkr, vol. 87, pp. 527-570, 1929. | |
| dc.relation.references | [21] E. D. Adrian, and B. H. C. Matthews, “The Berger rhythm: potential changes from the occipital lobes in man,” Brain: A Journal of Neurology, vol. 57, pp.355-385, 1934. | |
| dc.relation.references | [22] M. Peker, B. Sen, and D. Delen, “A Novel Method for Automated Diagnosis of Epilepsy Using Complex-Valued Classifiers,” IEEE Journal of Biomedical and Health Informatics vol. 20, no. 1, pp. 108–118, January 2016. | |
| dc.relation.references | [23] B. Sen, M. Peker, F.V. Celebi and A. Cavusoglu, “A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms,” Journal of Medical Systems, vol. 38, no. 18, 2014. doi: 10.1007/s10916-014-0018-0, | |
| dc.relation.references | [24] I. W.Selesnick, R. G.Baraniuk, and N. G.Kingsbury, "The Dual-Tree Complex Wavelet Transform",IEEE Signal Processing Magazine,Vol. 22, No 6, pp. 123–151, December 2005. | |
| dc.relation.references | [25] R. G. Andrzejak, G. Widman, K. Lehnertz, C. Rieke, P. David and C. E. Elger, “The epileptic process as nonlinear deterministic dynamics in a stochastic environment: an evaluation on mesial temporal lobe epilepsy,” Epilepsy Research, vol. 44, pp. 129-140, 2001. | |
| dc.relation.references | [26] S. Trampitsch, “Complex-Valued Data Estimation”, Master Thesis, Alpen-Adria-Universität Klagenfurt, Fakultät für Technische Wissenschaften, Austria, 2013, available online at http://www.uniklu.ac.at/tewi/downloads/masterthesis_TrampitschStefan_Final_Version.pdf | |
| dc.relation.references | [27] T. Adili, P. Schreier, and L.L. Scharf. “Complex-valued signal processing, The proper way to deal with impropriety,” IEEE Transactions on Signal Processing, vol. 59, no. 11, pp. 5101-5125, November 2011. | |
| dc.relation.references | [28] S. M. Kay. Statistical Signal Processing: Estimation Theory, volume 1. Prentice Hall PTR, 2010. | |
| dc.relation.references | [29] P. J. Schreier and L. L. Scharf. “Second-order analysis of improper complexrandom vectors and processes,” IEEE Transactions on Signal Processing, vol.51, no. 3, pp.s 714-725, March 2003. | |
| dc.relation.references | [30] I. Aizenberg, “Hebbian and Error-Correction Learning for ComplexValued Neurons,” Soft Computing, vol. 17, no. 2, pp. 265-273, Feb. 2013. | |
| dc.relation.references | [31] I. Aizenberg, N. Aizenberg, C. Butakov, and E. Farberov, “Image Recognition on the Neural Network based on Multi-Valued Neurons,” Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, IEEE Computer Society Press, vol. 2. pp. 993-996, September 3-8, 2000. | |
| dc.relation.referencesen | [1] A. Hirose, Complex-Valued Neural Networks, 2nd Edn.. Springer, Berlin, Heidelberg, 2012. | |
| dc.relation.referencesen | [2] D. Mandic and V. Su Lee Goh, Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models. John Wiley & Sons, 2009. | |
| dc.relation.referencesen | [3] I. Aizenberg, I., Complex-Valued Neural Networks with MultiValued Neurons. Berlin: Springer-Verlag Publishers, 2011. | |
| dc.relation.referencesen | [4] Y.Nakano, and A.Hirose, "Improvement of Plastic Landmine Visualization Performance by use of ring-CSOM and FrequencyDomain Local Correlation," IEICE Transactions on Electronics, vol. E92-C, iss. 1, pp. 102-108, Jan. 2009. | |
| dc.relation.referencesen | [5] S. L. Goh, M. Chen, D. H. Popovic, K. Aihara, D. Obradovic and D. P. Mandic, "Complex Valued Forecasting of Wind Profile,"Renewable Energy, vol. 31 , pp. 1733-1750, Sep. 2006. | |
| dc.relation.referencesen | [6] A. Handayani, A.B.Suksmono, T.L.R.Mengko, and A.Hirose, "Blood Vessel Segmentation in Complex-Valued Magnetic Resonance Images with Snake Active Contour Model," International Journal of E-Health and Medical Communications, vol. 1, iss. 1, pp. 41-52, Jan. 2010. | |
| dc.relation.referencesen | [7] I. Aizenberg, L. Sheremetov, L. Villa-Vargas, and J. MartinezMuñoz, "Multilayer Neural Network with Multi-Valued Neurons in Time Series Forecasting of Oil Production," Neurcomputing, vol. 175, part B, pp. 980-989, Jan. 2016. | |
| dc.relation.referencesen | [8] N.V.Manyakov, I. Aizenberg, N. Chumerin, and M. Van Hulle, "Phase-Coded Brain-Computer Interface Based on MLMVN", book chapter in Complex-Valued Neural Networks: Advances and Applications (A. Hirose – Ed.), Wiley, 2012, pp. 185-208. | |
| dc.relation.referencesen | [9] I. Aizenberg, C. Moraga, and D. Paliy, "A Feedforward Neural Network based on Multi-Valued Neurons", In Computational Intelligence, Theory and Applications. Advances in Soft Computing , XIV, (B. Reusch - Ed.), Springer, Berlin, Heidelberg, New York, pp. 599-612, 2005. | |
| dc.relation.referencesen | [10] I. Aizenberg, and C. Moraga, "Multilayer Feedforward Neural Network based on Multi-Valued Neurons (MLMVN) and a backpropagation learning algorithm," Soft Computing, 11, iss. 2, pp. 169-183, Jan. 2007. | |
| dc.relation.referencesen | [11] N. N. Aizenberg, Yu. L. Ivaskiv, D. A. Pospelov, and G.F. Hudiakov, "Multivalued Threshold Functions. Synthesis of Multivalued Threshold Elements," Cybernetics and Systems Analysis, vol. 9, no. 1, pp. 61-77, January 1973. | |
| dc.relation.referencesen | [12] N.N. Aizenberg and I.N. Aizenberg, "CNN Based on Multi-Valued Neuron as a Model of Associative Memory for Gray-Scale Images," Proceedings of the Second IEEE International Workshop on Cellular Neural Networks and their Applications, Munich, pp.36-41, October 14-16, 1992. | |
| dc.relation.referencesen | [13] I. Aizenberg, D. Paliy, J. Zurada, and J. Astola, "Blur Identification by Multilayer Neural Network based on Multi-Valued Neurons", IEEE Transactions on Neural Networks, vol. 19, no. 5, pp. 883-898, May 2008. | |
| dc.relation.referencesen | [14] O. Fink, E. Zio, and U. Weidmann, "Predicting Component Reliability and Level of Degradation with Complex-Valued Neural Networks," Reliability Engineering & System Safety, vol. 121, pp. 198–206, 2014. | |
| dc.relation.referencesen | [15] I. Aizenberg, A. Luchetta and S. Manetti, "A modified learning algorithm for the multilayer neural network with multi-valued neurons based on the complex QR decomposition," Soft Computing, vol. 16, iss. 4, pp. 563-575, Apr. 2012. | |
| dc.relation.referencesen | [16] I. Aizenberg, "MLMVN with Soft Margins Learning," IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 9, pp. 1632-1644, September 2014. | |
| dc.relation.referencesen | [17] I. Aizenberg, A. Luchetta, S. Manetti., and C. Piccirilli., "System Identification using FRA and a modified MLMVN with Arbitrary Complex-Valued Inputs," Proceedings of the 2016 IEEE International Joint Conference on Neural Networks (IJCNN 2016), Vancouver, pp. 4404-4411, July, 2016. | |
| dc.relation.referencesen | [18] E. Aizenberg and I. Aizenberg, "Batch LLS-based Learning Algorithm for MLMVN with Soft Margins," IEEE Symposium Series of Computational Intelligence (SSCI-2014), pp. 48-55, December, 2014. | |
| dc.relation.referencesen | [19] R. Caton, "The electric currents of brain", British Medical Journal, vol. 2, pp. 278, 1875. | |
| dc.relation.referencesen | [20] H. Berger, "Über das Elektroenkephalogramm des Menschen", Arch Psychiatr Nervenkr, vol. 87, pp. 527-570, 1929. | |
| dc.relation.referencesen | [21] E. D. Adrian, and B. H. C. Matthews, "The Berger rhythm: potential changes from the occipital lobes in man," Brain: A Journal of Neurology, vol. 57, pp.355-385, 1934. | |
| dc.relation.referencesen | [22] M. Peker, B. Sen, and D. Delen, "A Novel Method for Automated Diagnosis of Epilepsy Using Complex-Valued Classifiers," IEEE Journal of Biomedical and Health Informatics vol. 20, no. 1, pp. 108–118, January 2016. | |
| dc.relation.referencesen | [23] B. Sen, M. Peker, F.V. Celebi and A. Cavusoglu, "A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms," Journal of Medical Systems, vol. 38, no. 18, 2014. doi: 10.1007/s10916-014-0018-0, | |
| dc.relation.referencesen | [24] I. W.Selesnick, R. G.Baraniuk, and N. G.Kingsbury, "The Dual-Tree Complex Wavelet Transform",IEEE Signal Processing Magazine,Vol. 22, No 6, pp. 123–151, December 2005. | |
| dc.relation.referencesen | [25] R. G. Andrzejak, G. Widman, K. Lehnertz, C. Rieke, P. David and C. E. Elger, "The epileptic process as nonlinear deterministic dynamics in a stochastic environment: an evaluation on mesial temporal lobe epilepsy," Epilepsy Research, vol. 44, pp. 129-140, 2001. | |
| dc.relation.referencesen | [26] S. Trampitsch, "Complex-Valued Data Estimation", Master Thesis, Alpen-Adria-Universität Klagenfurt, Fakultät für Technische Wissenschaften, Austria, 2013, available online at http://www.uniklu.ac.at/tewi/downloads/masterthesis_TrampitschStefan_Final_Version.pdf | |
| dc.relation.referencesen | [27] T. Adili, P. Schreier, and L.L. Scharf. "Complex-valued signal processing, The proper way to deal with impropriety," IEEE Transactions on Signal Processing, vol. 59, no. 11, pp. 5101-5125, November 2011. | |
| dc.relation.referencesen | [28] S. M. Kay. Statistical Signal Processing: Estimation Theory, volume 1. Prentice Hall PTR, 2010. | |
| dc.relation.referencesen | [29] P. J. Schreier and L. L. Scharf. "Second-order analysis of improper complexrandom vectors and processes," IEEE Transactions on Signal Processing, vol.51, no. 3, pp.s 714-725, March 2003. | |
| dc.relation.referencesen | [30] I. Aizenberg, "Hebbian and Error-Correction Learning for ComplexValued Neurons," Soft Computing, vol. 17, no. 2, pp. 265-273, Feb. 2013. | |
| dc.relation.referencesen | [31] I. Aizenberg, N. Aizenberg, C. Butakov, and E. Farberov, "Image Recognition on the Neural Network based on Multi-Valued Neurons," Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, IEEE Computer Society Press, vol. 2. pp. 993-996, September 3-8, 2000. | |
| dc.citation.conference | IEEE second international conference "Data stream mining and processing" | |
| dc.citation.spage | 392 | |
| dc.citation.epage | 396 | |
| dc.coverage.placename | Львів | |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference
|
