Please use this identifier to cite or link to this item: https://doi.org/10.1162/NECO_a_00500
Title: Discriminative learning of propagation and spatial pattern formotor imagery EEG analysis
Authors: Li, X.
Zhang, H.
Guan, C.
Ong, S.H. 
Ang, K.K.
Pan, Y.
Issue Date: 2013
Source: Li, X.,Zhang, H.,Guan, C.,Ong, S.H.,Ang, K.K.,Pan, Y. (2013). Discriminative learning of propagation and spatial pattern formotor imagery EEG analysis. Neural Computation 25 (10) : 2709-2733. ScholarBank@NUS Repository. https://doi.org/10.1162/NECO_a_00500
Abstract: Effective learning and recovery of relevant source brain activity patterns is a major challenge to brain-computer interface using scalp EEG.Various spatial filtering solutions have been developed. Most current methods estimate an instantaneous demixing with the assumption of uncorrelatedness of the source signals. However, recent evidence in neuroscience suggests that multiple brain regions cooperate, especially during motor imagery, a major modality of brain activity for brain-computer interface. In this sense, methods that assume uncorrelatedness of the sources become inaccurate. Therefore, we are promoting a new methodology that considers both volume conduction effect and signal propagation between multiple brain regions. Specifically, we propose a novel discriminative algorithm for joint learning of propagation and spatial pattern with an iterative optimization solution. To validate the new methodology, we conduct experiments involving 16 healthy subjects and perform numerical analysis of the proposed algorithm for EEG classification in motor imagery brain-computer interface. Results from extensive analysis validate the effectiveness of the new methodology with high statistical significance. © 2013 Massachusetts Institute of Technology.
Source Title: Neural Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/55668
ISSN: 08997667
DOI: 10.1162/NECO_a_00500
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