Decoding phase-based information from steady-state visual evoked potentials with use of complex-valued neural network

Nikolay V. Manyakov; Nikolay Chumerin; Adrien Combaz; Arne Robben; Marijn van Vliet; Marc M. Van Hulle

doi:10.1007/978-3-642-23878-9_17

Decoding phase-based information from steady-state visual evoked potentials with use of complex-valued neural network

Nikolay V. Manyakov^*, Nikolay Chumerin, Adrien Combaz, Arne Robben, Marijn van Vliet, Marc M. Van Hulle

^*Corresponding author for this work

Not published at Aalto University

KU Leuven

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

8 Citations (Scopus)

Abstract

In this paper, we report on the decoding of phase-based information from steady-state visual evoked potential (SSVEP) recordings by means of a multilayer feedforward neural network based on multi-valued neurons. Networks of this kind have inputs and outputs which are well fitted for the considered task. The dependency of the decoding accuracy w.r.t. the number of targets and the decoding window size is discussed. Comparing existing phase-based SSVEP decoding methods with the proposed approach, we show that the latter performs better for the larger amount of target classes and the sufficient size of decoding window. The necessity of the proper frequency selection for each subject is discussed.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning, IDEAL 2011 - 12th International Conference, Proceedings
Pages	135-143
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-642-23878-9_17
Publication status	Published - 26 Sept 2011
MoE publication type	A4 Conference publication
Event	International Conference on Intelligent Data Engineering and Automated Learning - Norwich, United Kingdom Duration: 7 Sept 2011 → 9 Sept 2011 Conference number: 12

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6936 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Intelligent Data Engineering and Automated Learning
Abbreviated title	IDEAL
Country/Territory	United Kingdom
City	Norwich
Period	07/09/2011 → 09/09/2011

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10.1007/978-3-642-23878-9_17

Cite this

Manyakov, N. V., Chumerin, N., Combaz, A., Robben, A., van Vliet, M., & Van Hulle, M. M. (2011). Decoding phase-based information from steady-state visual evoked potentials with use of complex-valued neural network. In Intelligent Data Engineering and Automated Learning, IDEAL 2011 - 12th International Conference, Proceedings (pp. 135-143). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6936 LNCS). https://doi.org/10.1007/978-3-642-23878-9_17

Manyakov, Nikolay V. ; Chumerin, Nikolay ; Combaz, Adrien et al. / Decoding phase-based information from steady-state visual evoked potentials with use of complex-valued neural network. Intelligent Data Engineering and Automated Learning, IDEAL 2011 - 12th International Conference, Proceedings. 2011. pp. 135-143 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper, we report on the decoding of phase-based information from steady-state visual evoked potential (SSVEP) recordings by means of a multilayer feedforward neural network based on multi-valued neurons. Networks of this kind have inputs and outputs which are well fitted for the considered task. The dependency of the decoding accuracy w.r.t. the number of targets and the decoding window size is discussed. Comparing existing phase-based SSVEP decoding methods with the proposed approach, we show that the latter performs better for the larger amount of target classes and the sufficient size of decoding window. The necessity of the proper frequency selection for each subject is discussed.",

author = "Manyakov, {Nikolay V.} and Nikolay Chumerin and Adrien Combaz and Arne Robben and {van Vliet}, Marijn and {Van Hulle}, {Marc M.}",

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Manyakov, NV, Chumerin, N, Combaz, A, Robben, A, van Vliet, M & Van Hulle, MM 2011, Decoding phase-based information from steady-state visual evoked potentials with use of complex-valued neural network. in Intelligent Data Engineering and Automated Learning, IDEAL 2011 - 12th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6936 LNCS, pp. 135-143, International Conference on Intelligent Data Engineering and Automated Learning, Norwich, United Kingdom, 07/09/2011. https://doi.org/10.1007/978-3-642-23878-9_17

Decoding phase-based information from steady-state visual evoked potentials with use of complex-valued neural network. / Manyakov, Nikolay V.; Chumerin, Nikolay; Combaz, Adrien et al.
Intelligent Data Engineering and Automated Learning, IDEAL 2011 - 12th International Conference, Proceedings. 2011. p. 135-143 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6936 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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AB - In this paper, we report on the decoding of phase-based information from steady-state visual evoked potential (SSVEP) recordings by means of a multilayer feedforward neural network based on multi-valued neurons. Networks of this kind have inputs and outputs which are well fitted for the considered task. The dependency of the decoding accuracy w.r.t. the number of targets and the decoding window size is discussed. Comparing existing phase-based SSVEP decoding methods with the proposed approach, we show that the latter performs better for the larger amount of target classes and the sufficient size of decoding window. The necessity of the proper frequency selection for each subject is discussed.

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Manyakov NV, Chumerin N, Combaz A, Robben A, van Vliet M, Van Hulle MM. Decoding phase-based information from steady-state visual evoked potentials with use of complex-valued neural network. In Intelligent Data Engineering and Automated Learning, IDEAL 2011 - 12th International Conference, Proceedings. 2011. p. 135-143. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-23878-9_17

Decoding phase-based information from steady-state visual evoked potentials with use of complex-valued neural network

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