Paper Info
Title
Wavelet Lifting Over Information-Based Eeg Graphs For Motor Imagery Data Classification
Abstract
The imagination of limb movements offers an intuitive paradigm for the control of electronic devices via brain computer interfacing (BCI). The analysis of electroencephalographic (EEG) data related to motor imagery potentials has proved to be a difficult task. EEG readings are noisy, and the elicited patterns occur in different parts of the scalp, at different instants and at different frequencies. Wavelet transform has been widely used in the BCI field as it offers temporal and spectral capabilities, although it lacks spatial information. In this study we propose a tailored second generation wavelet to extract features from these three domains. This transform is applied over a graph representation of motor imaginary trials, which encodes temporal and spatial information. This graph is enhanced using per-subject knowledge in order to optimise the spatial relationships among the electrodes, and to improve the filter design. This method improves the performance of classifying different imaginary limb movements maintaining the low computational resources required by the lifting transform over graphs. By using an online dataset we were able to positively assess the feasibility of using the novel method in an online BCI context.
Year
DOI
Venue
2014
10.1007/978-3-662-45686-6_1
PHYSIOLOGICAL COMPUTING SYSTEMS, PHYCS 2014
Keywords
Field
DocType
Multiresolution analysis, EEG data graph representation, Motor imagery, Brain computer interfacing, Wavelet lifting, Mutual information
Computer vision,Computer science,Brain–computer interface,Multiresolution analysis,Artificial intelligence,Mutual information,Data classification,Graph (abstract data type),Wavelet,Wavelet transform,Motor imagery
Conference
Volume
ISSN
Citations 
8908
0302-9743
1
PageRank 
References 
Authors
0.39
7
3
Name
Order
Citations
PageRank
Javier Asensio-Cubero1172.84
John Q. Gan2184.87
Palaniappan, R.3316.13