A BCI motor imagery experiment based on parametric feature extraction and Fisher Criterion. D'Croz-Baron, D., Ramirez, J., M., Baker, M., Alarcon-Aquino, V., & Carrera, O. In CONIELECOMP 2012, 22nd International Conference on Electrical Communications and Computers, pages 257-261, 2, 2012. IEEE.
A BCI motor imagery experiment based on parametric feature extraction and Fisher Criterion [link]Website  abstract   bibtex   
An EEG-based classification method in the time domain is proposed to identify left and right hand motor imagery as part of a brain-computer interface (BCI) experiment. The feature vector is formed by sixth order autoregressive coefficients (AR) or sixth order adaptive autoregressive coefficients (AAR) representing EEG signals obtained from C3 and C4 channels, according to the EEG 10-20 standard. The signal is analyzed considering 1 second windows with a 50% overlapping. A feature selection process based on the Fisher Criterion (FC) removes irrelevant or noisy information. A Linear Discriminant Analysis (LDA) is applied to both cases: feature vectors formed with the total number of coefficients, and feature vectors formed with coefficients corresponding to larger Fisher Ratio. Classification results obtained using two AR methods, Burg and Levinson-Durbin, and one AAR LMS are presented. © 2012 IEEE.
@inproceedings{
 title = {A BCI motor imagery experiment based on parametric feature extraction and Fisher Criterion},
 type = {inproceedings},
 year = {2012},
 identifiers = {[object Object]},
 keywords = {Adaptive Autoregressive Coefficients (AAR),Autoregressive coefficients (AR),Brain Computer Interfaces (BCI),EEG,Fisher Criterion (FC)},
 pages = {257-261},
 websites = {http://ieeexplore.ieee.org/document/6189920/},
 month = {2},
 publisher = {IEEE},
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 abstract = {An EEG-based classification method in the time domain is proposed to identify left and right hand motor imagery as part of a brain-computer interface (BCI) experiment. The feature vector is formed by sixth order autoregressive coefficients (AR) or sixth order adaptive autoregressive coefficients (AAR) representing EEG signals obtained from C3 and C4 channels, according to the EEG 10-20 standard. The signal is analyzed considering 1 second windows with a 50% overlapping. A feature selection process based on the Fisher Criterion (FC) removes irrelevant or noisy information. A Linear Discriminant Analysis (LDA) is applied to both cases: feature vectors formed with the total number of coefficients, and feature vectors formed with coefficients corresponding to larger Fisher Ratio. Classification results obtained using two AR methods, Burg and Levinson-Durbin, and one AAR LMS are presented. © 2012 IEEE.},
 bibtype = {inproceedings},
 author = {D'Croz-Baron, David and Ramirez, Juan Manuel and Baker, Mary and Alarcon-Aquino, Vicente and Carrera, Obed},
 booktitle = {CONIELECOMP 2012, 22nd International Conference on Electrical Communications and Computers}
}

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