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Authors: Javier Asensio-Cubero 1 ; John Q. Gan 1 and Ramaswamy Palaniappan 2

Affiliations: 1 University of Essex, United Kingdom ; 2 University of Wolverhampton, United Kingdom

ISBN: 978-989-758-006-2

Keyword(s): Multiresolution Analysis, EEG Data Graph Representation, Motor Imagery, Brain Computer Interfacing, Wavelet Lifting, Mutual Information.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Devices for Computer Interaction ; Biomedical Engineering ; Biomedical Instruments and Devices ; Biomedical Signal Processing ; Biosignal Acquisition, Analysis and Processing ; Brain-Computer Interfaces ; Data Manipulation ; Devices ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: Brain computer interfaces are control systems that allow the interaction with electronic devices by analysing the user’s brain activity. The analysis of brain signals, more concretely, electroencephalographic data, represents a big challenge due to its noisy and low amplitude nature. Many researchers in the field have applied wavelet transform in order to leverage the signal analysis benefiting from its temporal and spectral capabilities. In this study we make use of the so-called second generation wavelets to extract features from temporal, spatial and spectral domains. The complete multiresolution analysis operates over an enhanced graph representation of motor imaginary trials, which uses per-subject knowledge to optimise the spatial links among the electrodes and to improve the filter design. As a result we obtain a novel method that improves the performance of classifying different imaginary limb movements without compromising the low computational resources used by lifting tran sform over graphs. (More)

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Paper citation in several formats:
Asensio-Cubero J., Q. Gan J. and Palaniappan R. (2014). Multiresolution Analysis of an Information based EEG Graph Representation for Motor Imagery Brain Computer Interfaces.In Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-006-2, pages 5-12. DOI: 10.5220/0004704200050012

@conference{phycs14,
author={Javier Asensio-Cubero and John Q. Gan and Ramaswamy Palaniappan},
title={Multiresolution Analysis of an Information based EEG Graph Representation for Motor Imagery Brain Computer Interfaces},
booktitle={Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS,},
year={2014},
pages={5-12},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004704200050012},
isbn={978-989-758-006-2},
}

TY - CONF

JO - Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Multiresolution Analysis of an Information based EEG Graph Representation for Motor Imagery Brain Computer Interfaces
SN - 978-989-758-006-2
AU - Asensio-Cubero J.
AU - Q. Gan J.
AU - Palaniappan R.
PY - 2014
SP - 5
EP - 12
DO - 10.5220/0004704200050012

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