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Classifying motor imagery EEG by Empirical Mode Decomposition based on spatial-time-frequency joint analysis approach

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3 Author(s)
Pengfei Wei ; Res. Centre for Neural Eng., Shenzhen Institutes of Adv. Technol., Shenzhen, China ; Qiuhua Li ; Guanglin Li

A novel spatial-time-frequency approach to classify the different mental task in brain computer interface was presented. A high resolution time-frequency spectral was achieved by using empirical mode decomposition and Hilbert-Huang transform, and the subject specific spatial-time-frequency joint features were extracted from the restricted spectral of multi-channel EEG recordings. A weighting synthetic classifier was built and used to identify the classes of the imaged motions The test results in four subjects showed that the classification accuracy varied between 77.0% and 95.0%, with an average of 85.9%, which suggested that the present method can achieve a reasonable performance in identifying imaged motions compared with previous methods.

Published in:

BioMedical Information Engineering, 2009. FBIE 2009. International Conference on Future

Date of Conference:

13-14 Dec. 2009