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
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 tra
nsform over graphs.
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