DocumentCode :
2498668
Title :
EEG signal classification using time-varying autoregressive models and common spatial patterns
Author :
Gutiérrez, D. ; Salazar-Varas, R.
Author_Institution :
Center of Res. & Adv. Studies, Cinvestav at Monterrey, Apodaca, Mexico
fYear :
2011
fDate :
Aug. 30 2011-Sept. 3 2011
Firstpage :
6585
Lastpage :
6588
Abstract :
The performance of EEG signal classification methods based on Common Spatial Patterns (CSP) depends on the operational frequency bands of the events to be discriminated. This problem has been recently addressed by using a sub-band decomposition of the EEG signals through filter banks. Even though this approach has proven effective, the performance still depends on the number of filters that are stacked and the criteria used to determine their cutoff frequencies. Therefore, we propose an alternative approach based on an eigenstructure decomposition of the signals´ time-varying autoregressive (TVAR) models. The eigen-based decomposition of the TVAR representation allows for subject-specific estimation of the principal time-varying frequencies, then such principal eigencomponents can be used in the traditional CSP-based classification. A series of simulations show that the proposed classification scheme can achieve high classification rates under realistic conditions, such as low signal-to-noise ratio (SNR), a reduced number of training experiments, and a reduced number of sensors used in the measurements.
Keywords :
autoregressive processes; eigenvalues and eigenfunctions; electroencephalography; medical signal processing; signal classification; spatial filters; time-varying systems; EEG signal classification; cutoff frequencies; eigenstructure decomposition; filter banks; operational frequency bands; signal-to-noise ratio; spatial patterns; sub-band decomposition; time-varying autoregressive models; Brain computer interfaces; Brain models; Electroencephalography; Noise; Sensors; Time frequency analysis; Algorithms; Brain; Brain Mapping; Electroencephalography; Humans; Man-Machine Systems; Models, Statistical; Motor Cortex; Regression Analysis; Reproducibility of Results; Signal Processing, Computer-Assisted; Signal-To-Noise Ratio; Software; Time Factors; User-Computer Interface;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE
Conference_Location :
Boston, MA
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4121-1
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2011.6091624
Filename :
6091624
Link To Document :
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