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Mot robust cross-subject klassificering av electroencephalogram (EEG) baserad brain-computer interfacing (BCI):En genomförbarhetsstudie
KTH, School of Engineering Sciences (SCI).
2019 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Towards robust cross-subject classification of electroencephalogram (EEG) patterns for brain-computer interfacing (BCI):A feasibility study (English)
Abstract [sv]

Brain-computer interface(BCI) är ett system där man kan skicka kommandon till dator med bara hjärnaktivitet. En sådan system är viktigt för människor lider av flera motorisk funktionshinder, då maskinen skulle kunna förbättra patienters liv genom att uppfylla deras behov. Denna rapport fokusera på en variant av BCI, kallas motor imagery based BCI, vilken basera på att klassificera försökspersons hjärnaktivitet då han/hon tänka sig att röra sin kroppsdelar. Det finns flera svårighet för att bygga en fungerande system, en av de är generalisering av tränad model. En tränad model garanti inte exakthet på annat försöksperson eller annat session. Även i samma session, kan model ger sämre resultat på grund av hjärnaktiviteten nonstationary natur. Denna rapport försöka hantera inter-subject klassificering problem med adaptive importance weighted linear discriminant analysis(AIWLDA), som gav bra resultat i både intra-session och inter-session klassificering av offline EEG baserad BCI. Det kommer visa i resultat att det finns försökspersons par där inter-subject generalisering är möjligt och AIWLDA kan avslöja mer av sådana par, men misslyckas att bevisa om det denna egenskap finns mellan alla försöksperson.

Abstract [en]

A brain-computer interface (BCI) is a system that enables the subject to send commands with merely brain activity. Such interface is important for people affected by multiple motor disabilities, where BCI made it possible for machine to better understand the patient and thus fulfill their demands. The BCI variante that base on motor imagery require classification on subject’s brain activity on imagining movement of body parts, which could be done by using different classifier. There exists multiple difficulty when developing such an system, one of them is generalization of trained models, this accuracy of trained model could not be guaranteed when using on a different subject or in a different session. Even within the same session, the classification result is not optimal due to brain activity’s non-stationary nature. This paper tackle the problem of intersubject classification with adaptive importance weighted linear discriminant analysis(AIWLDA), which shows promising result on both intersession and intra-session classification of offline EEG based BCI. This research has shown that there exist subject pairs with inter-subject generalizable potential, more pairs could be revealed by using AIWLDA, but this method fail to robustly classify across every subject-pairs.

Place, publisher, year, edition, pages
2019.
Series
TRITA-SCI-GRU ; 2019:123
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-254765OAI: oai:DiVA.org:kth-254765DiVA, id: diva2:1335195
Supervisors
Examiners
Available from: 2019-07-04 Created: 2019-07-04 Last updated: 2019-07-04Bibliographically approved

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