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Classification of Electroencephalographic Signalsfor Brain-Computer Interface
KTH, School of Computer Science and Communication (CSC).
2013 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Brain-computer interface is a promising research area that has the potential to aid

impaired individuals in their daily lives. There are several different methods for

capturing brain signals, both invasive and noninvasive. A popular noninvasive

technique is electroencephalography (EEG). It is of great interest to be able to

interpret EEG signals accurately so that a machine can carry out correct instructions.

This paper looks at different machine learning techniques, both linear and nonlinear,

in an attempt to classify EEG signals. It is found that support vector machines provide

more satisfactory results than neural networks.

Place, publisher, year, edition, pages
Kandidatexjobb CSC, K13062
National Category
Computer Science
URN: urn:nbn:se:kth:diva-136678OAI: diva2:676642
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2013-12-13 Created: 2013-12-06 Last updated: 2013-12-13Bibliographically approved

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Classification of Electroencephalographic Signals(558 kB)113 downloads
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