Skip to main content
Kent Academic Repository

Multiresolution analysis over simple graphs for brain computer interfaces

Asensio-Cubero, J, Gan, J Q, Palaniappan, Ramaswamy (2013) Multiresolution analysis over simple graphs for brain computer interfaces. Journal of Neural Engineering, 10 (4). 046014. ISSN 1741-2560. (doi:10.1088/1741-2560/10/4/046014) (KAR id:50389)

PDF (Accepted version (12 months embargo expired)) Author's Accepted Manuscript
Language: English
Download this file
(PDF/1MB)
[thumbnail of Accepted version (12 months embargo expired)]
Preview
Request a format suitable for use with assistive technology e.g. a screenreader
Official URL:
http://dx.doi.org/10.1088/1741-2560/10/4/046014

Abstract

OBJECTIVE:

Multiresolution analysis (MRA) offers a useful framework for signal analysis in the temporal and spectral domains, although commonly employed MRA methods may not be the best approach for brain computer interface (BCI) applications. This study aims to develop a new MRA system for extracting tempo-spatial-spectral features for BCI applications based on wavelet lifting over graphs.

APPROACH:

This paper proposes a new graph-based transform for wavelet lifting and a tailored simple graph representation for electroencephalography (EEG) data, which results in an MRA system where temporal, spectral and spatial characteristics are used to extract motor imagery features from EEG data. The transformed data is processed within a simple experimental framework to test the classification performance of the new method.

MAIN RESULTS:

The proposed method can significantly improve the classification results obtained by various wavelet families using the same methodology. Preliminary results using common spatial patterns as feature extraction method show that we can achieve comparable classification accuracy to more sophisticated methodologies. From the analysis of the results we can obtain insights into the pattern development in the EEG data, which provide useful information for feature basis selection and thus for improving classification performance.

SIGNIFICANCE:

Applying wavelet lifting over graphs is a new approach for handling BCI data. The inherent flexibility of the lifting scheme could lead to new approaches based on the hereby proposed method for further classification performance improvement.

Item Type: Article
DOI/Identification number: 10.1088/1741-2560/10/4/046014
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.9.H85 Human computer interaction
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Palaniappan Ramaswamy
Date Deposited: 03 Sep 2015 17:12 UTC
Last Modified: 16 Nov 2021 10:21 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50389 (The current URI for this page, for reference purposes)

University of Kent Author Information

Palaniappan, Ramaswamy.

Creator's ORCID: https://orcid.org/0000-0001-5296-8396
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.