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Classification of Electroencephalographic SignalsFor Brain-Computer Interface
KTH, School of Computer Science and Communication (CSC).
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 (BCI) can be used for example to

help disabled people to control a computer without the use

of mouse or keyboard. The brain signals beta and mu are

acquired by electroencephalography (EEG) and shows what

parts of the brain that are active not only at the performing

of a muscular movement, but also by thinking about

it. By analyzing EEG-signals with the methods linear discriminant

analysis and artificial neural networks the aim is

to explore which of two possible cognitive tasks a subject

is performing. In the essay these methods are compared

with aspect to correct classifications. In conclusion, when

performing binary classification of mu and beta waves, a

small multi layer perception is sufficient.

Abstract [sv]

Hjärna-datorgränssnitt (brain-computer interface, BCI) kan

användas för att exempelvis hjälpa svårt funktionsnedsatta

människor att styra en dator utan att använda mus eller

tangentbord. Hjärnsignalerna beta och my erhålls via

electroencefalografi (EEG) och visar vilka delar av hjärnan

som är aktiva inte bara vid utförandet av muskelrörelser

utan även vid tanken därpå. Genom att analysera

EEG-signalerna med metoderna linjär diskriminantanalys

och artificiellt neuralt nätverk är syftet att undersöka vilken

av två möjliga kognitiva uppgifter en försöksperson utför.

I uppsatsen jämförs dessa metoder med avseende på korrekta

klassificering. Som slutsats kan sägas att vid binär

klassifikation av beta- och my-signaler är minsta möjliga

flerlagersperceptron tillräcklig.

Place, publisher, year, edition, pages
2013.
Series
Kandidatexjobb CSC, K13063
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-136679OAI: oai:DiVA.org:kth-136679DiVA: diva2:676643
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2013-12-13 Created: 2013-12-06 Last updated: 2013-12-13Bibliographically approved

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http://www.csc.kth.se/utbildning/kth/kurser/DD143X/dkand13/Group10Pawel/report/RichardN_report.pdf
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School of Computer Science and Communication (CSC)
Computer Science

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