title: Discriminative learning of propagation and spatial pattern for motor imagery EEG analysis.
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.
authors.affiliation: NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore 119613. a0068297@nus.edu.sg
authors.forename: Xinyang
authors.initials: X
authors.lastname: Li
authors.forename: Haihong
authors.initials: H
authors.lastname: Zhang
authors.forename: Cuntai
authors.initials: C
authors.lastname: Guan
authors.forename: Sim Heng
authors.initials: SH
authors.lastname: Ong
authors.forename: Kai Keng
authors.initials: KK
authors.lastname: Ang
authors.forename: Yaozhang
authors.initials: Y
authors.lastname: Pan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D062207:Brain-Computer Interfaces; D003627:Data Interpretation, Statistical; D004193:Discrimination Learning / Q000502:physiology*; D004569:Electroencephalography / Q000706:statistics & numerical data*; D006801:Humans; D007092:Imagination / Q000502:physiology*; D007858:Learning; D015233:Models, Statistical; D009068:Movement / Q000502:physiology*; D009488:Neurosciences; D012815:Signal Processing, Computer-Assisted