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Computational Intelligence and Neuroscience
Volume 2013, Article ID 537218, 12 pages
http://dx.doi.org/10.1155/2013/537218
Research Article

Common Spatio-Time-Frequency Patterns for Motor Imagery-Based Brain Machine Interfaces

1Department of Electronic and Information Engineering, Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei-shi, Tokyo 184-8588, Japan
2RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0106, Japan

Received 7 May 2013; Revised 17 August 2013; Accepted 31 August 2013

Academic Editor: Daoqiang Zhang

Copyright © 2013 Hiroshi Higashi and Toshihisa Tanaka. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.