Multiresolution analysis over graphs for a motor imagery based online BCI game

• Published in: Computers in biology and medicine Vol. 68; pp. 21 - 26
• Main Authors: Asensio-Cubero, Javier, Gan, John Q., Palaniappan, Ramaswamy
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.01.2016; Elsevier Limited
• Subjects: Attention Deficit Hyperactivity Disorder; BCI game; Brain-Computer Interfaces; Classification; Data analysis; EEG graph representation; Electrodes; Games; Graph representations; Humans; Imagination; Internal Medicine; Motor imagery; Other; Signal processing; Standard deviation; Video Games; Wavelet lifting; Motor imagery; EEG graph representation; Wavelet lifting; BCI game
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2015.10.016

Multiresolution analysis (MRA) over graph representation of EEG data has proved to be a promising method for offline brain–computer interfacing (BCI) data analysis. For the first time we aim to prove the feasibility of the graph lifting transform in an online BCI system. Instead of developing a pointer device or a wheel-chair controller as test bed for human–machine interaction, we have designed and developed an engaging game which can be controlled by means of imaginary limb movements. Some modifications to the existing MRA analysis over graphs for BCI have also been proposed, such as the use of common spatial patterns for feature extraction at the different levels of decomposition, and sequential floating forward search as a best basis selection technique. In the online game experiment we obtained for three classes an average classification rate of 63.0% for fourteen naive subjects. The application of a best basis selection method helps significantly decrease the computing resources needed. The present study allows us to further understand and assess the benefits of the use of tailored wavelet analysis for processing motor imagery data and contributes to the further development of BCI for gaming purposes.