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Harvard
Shahsavari, S. och Montes, H. (2006) Classifying of EEG Signals Recorded During Right and Left-hand Finger Movements. Göteborg : Chalmers University of Technology (Ex - Institutionen för signaler och system, Chalmers tekniska högskola, nr: EX033/2006).
BibTeX
@mastersthesis{
Shahsavari2006,
author={Shahsavari, Sima and Montes, Hector},
title={Classifying of EEG Signals Recorded During Right and Left-hand Finger Movements},
abstract={Brain Computer Interface (BCI) technology allows a person to control a device by bypassing the use of muscular activity. Signal processing and classification methods play a decisive role in the performance accuracy in BCI application. In this thesis extensive comparison among novel electroencephalic(EEG) pattern recognition methods is provided. Signals collected during left/right self-paced typing are analyzed and classified based on different schemes including Autoregressive and Exogenous Autoregressive model estimation, Smoothing and Time Averaging and Common Spatial Patterns (CSP) filtering. Comparison between methods
is performed mainly on the BCI 2002 and 2003 competition data sets available on the Internet and currently used by many researchers as etalon data sets. The proposed methods combining common spatial pattern filtering feature extraction and Mahalanobis distance classifier as well as
Support Vector Machines show the best performance.},
publisher={Institutionen för signaler och system, Chalmers tekniska högskola},
place={Göteborg},
year={2006},
series={Ex - Institutionen för signaler och system, Chalmers tekniska högskola, no: EX033/2006},
keywords={Brain Computer Interface (BCI), Event Related Potential (ERP), Event Related Synchronization/Desynchronization (ERS/ERD), spatial patterns, CSP, CSSD, single trial classification, electroencephalogram (EEG), finger movement, linear Fisher’s, discriminant, kernel Fisher discriminant, Gaussian mixture models, support vector machine, Quadratic Bayesian discriminant, Mahalanobis distance classifier},
note={80},
}
RefWorks
RT Generic
SR Electronic
ID 63223
A1 Shahsavari, Sima
A1 Montes, Hector
T1 Classifying of EEG Signals Recorded During Right and Left-hand Finger Movements
YR 2006
AB Brain Computer Interface (BCI) technology allows a person to control a device by bypassing the use of muscular activity. Signal processing and classification methods play a decisive role in the performance accuracy in BCI application. In this thesis extensive comparison among novel electroencephalic(EEG) pattern recognition methods is provided. Signals collected during left/right self-paced typing are analyzed and classified based on different schemes including Autoregressive and Exogenous Autoregressive model estimation, Smoothing and Time Averaging and Common Spatial Patterns (CSP) filtering. Comparison between methods
is performed mainly on the BCI 2002 and 2003 competition data sets available on the Internet and currently used by many researchers as etalon data sets. The proposed methods combining common spatial pattern filtering feature extraction and Mahalanobis distance classifier as well as
Support Vector Machines show the best performance.
PB Institutionen för signaler och system, Chalmers tekniska högskola,PB Institutionen för signaler och system, Chalmers tekniska högskola,
T3 Ex - Institutionen för signaler och system, Chalmers tekniska högskola, no: EX033/2006
LA eng
LK http://db.s2.chalmers.se/download/masters/master_033_2006.pdf
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