title: Common spatio-time-frequency patterns for motor imagery-based brain machine interfaces.
abstract: For efficient decoding of brain activities in analyzing brain function with an application to brain machine interfacing (BMI), we address a problem of how to determine spatial weights (spatial patterns), bandpass filters (frequency patterns), and time windows (time patterns) by utilizing electroencephalogram (EEG) recordings. To find these parameters, we develop a data-driven criterion that is a natural extension of the so-called common spatial patterns (CSP) that are known to be effective features in BMI. We show that the proposed criterion can be optimized by an alternating procedure to achieve fast convergence. Experiments demonstrate that the proposed method can effectively extract discriminative features for a motor imagery-based BMI.
authors.affiliation: Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei-shi, Tokyo 184-8588, Japan ; RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0106, Japan.
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Higashi
authors.forename: Toshihisa
authors.initials: T
authors.lastname: Tanaka
mesh_terms: D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D004569:Electroencephalography; D006801:Humans; D007092:Imagination / Q000502:physiology*; D009068:Movement / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted