In English

Classifying of EEG Signals Recorded During Right and Left-hand Finger Movements

Sima Shahsavari ; Hector Montes
Göteborg : Chalmers tekniska högskola, 2006. 80 s. Ex - Institutionen för signaler och system, Chalmers tekniska högskola, ISSN 99-2747920-4; EX033/2006, 2006.
[Examensarbete på avancerad nivå]

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.

Nyckelord: 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

Publikationen registrerades 2007-12-16. Den ändrades senast 2013-04-04

CPL ID: 63223

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