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

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dc.contributor.authorShahsavari, Sima
dc.contributor.authorMontes, Hector
dc.contributor.departmentChalmers tekniska högskola / Institutionen för signaler och systemsv
dc.contributor.departmentChalmers University of Technology / Department of Signals and Systemsen
dc.date.accessioned2019-07-03T12:07:12Z
dc.date.available2019-07-03T12:07:12Z
dc.date.issued2006
dc.description.abstractBrain 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.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/63223
dc.language.isoeng
dc.relation.ispartofseriesEx - Institutionen för signaler och system, Chalmers tekniska högskola : EX033/2006
dc.setspec.uppsokTechnology
dc.subjectIndustriell bioteknik
dc.subjectIndustrial Biotechnology
dc.titleClassifying of EEG Signals Recorded During Right and Left-hand Finger Movements
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
dc.type.uppsokH
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