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

Authors:
Nikolay V. Manyakov;Nikolay Chumerin;Adrien Combaz;Arne Robben;Marijn van Vliet;Marc M. Van Hulle
Affiliations:
Laboratory for Neuro- and Psychofysiology, K.U.Leuven, Leuven, Belgium;Laboratory for Neuro- and Psychofysiology, K.U.Leuven, Leuven, Belgium;Laboratory for Neuro- and Psychofysiology, K.U.Leuven, Leuven, Belgium;Laboratory for Neuro- and Psychofysiology, K.U.Leuven, Leuven, Belgium;Laboratory for Neuro- and Psychofysiology, K.U.Leuven, Leuven, Belgium;Laboratory for Neuro- and Psychofysiology, K.U.Leuven, Leuven, Belgium
Venue:
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Year:
2011

Citing 6
Cited 1

Multilayer Feedforward Neural Network Based on Multi-valued Neurons (MLMVN) and a Backpropagation Learning Algorithm

Soft Computing - A Fusion of Foundations, Methodologies and Applications
Impact of Frequency Selection on LCD Screens for SSVEP Based Brain-Computer Interfaces

IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Ultra-low-power biopotential interfaces and their applications in wearable and implantable systems

Microelectronics Journal
Complex-valued neurons with phase-dependent activation functions

ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Decoding stimulus-reward pairing from local field potentials recorded from monkey visual cortex

IEEE Transactions on Neural Networks
Blur Identification by Multilayer Neural Network Based on Multivalued Neurons

IEEE Transactions on Neural Networks

Brain-computer interface research at Katholieke Universiteit Leuven

Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies

Quantified Score

Hi-index	0.00

Visualization

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 multivalued 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.