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Genomförbarhetsstudie av att känna igen två tankemönster i följd med EEG
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2015 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Feasibility study of recognizing two subsequent thought patterns with EEG (English)
Abstract [sv]

Studien implementerade ett hjärna-dator-gränssnitt med hjälp av EEG-instrumentet MindWave Mobile Headset. Vi undersökte om det var möjligt att utföra fyra operationer genom att använda tankemönster. Fyra försökspersoner deltog i studien. Deras uppgift var att tänka i två tankemönster i följd som resulterade i en operation. EEG-signalen förbehandlas så att en mönsterigenkänningsmetod (k-NN) lättare kunde urskilja två tankemönster ur signalen. Denna undersökning har till vår vetskap inte tidigare utförts och är därmed kunskapsluckan vi ämnar fylla. Att fylla denna kunskapslucka är av intresse för bland annat användargrupperna: rörelsehindrade, spelintresserade och Virtual Reality-användare. Vi tog fram en modell som modellerade det bästa möjliga utfallet av metodiken i föreliggande studie. Undersökningens resultat kunde inte användas för att göra slutsatser angående frågeställningen då detta skulle vara att post hoc-teoretisera. I modellen visades dock tre av fyra operationer vara genomförbara, med en indikation om att även den fjärde var möjlig att utföra. Resultatet indikerar att det finns anledning att utföra en fortsatt studie. Den föreslagna fortsatta studien bör innefatta nya mätningar som testas av modellen för att fullt ut besvara problemformuleringen.

Abstract [en]

This study implements a Brain-Computer-Interface using the EEG-instrument MindWave Mobile Headset. We studied the feasibility of performing four operations using thought patterns. Four test subjects participated in the study. Their task was to think in two subsequent thought patterns that resulted in an operation. The EEG-signal was pre-processed in such a way that a pattern recognition algorithm (k-NN) more easily could recognize two thought patterns in the signal. This study has to our knowledge not been done before and thus aims to fill this lack of knowledge in the scientific community. User groups that have an interest in filling this gap are, amongst others; disabled people, gamers, and Virtual Reality users. We created a model that modeled the best possible outcome of the method used in this study. Conclusions drawn from the result can not be used to fully answer the problem statement, since it would be to post hoc-theorize. However, three out of four operations were possible to perform in the model, with an indication that the fourth also was possible to perform. These results indicate that there are grounds to continue this study. The proposed continued study should include new measurements that are tested by the model to determine if it is feasible to distinguish all four operations.

Place, publisher, year, edition, pages
2015. , 39 p.
Keyword [en]
EEG, MindWave, BCI, feature vector, pre-processing, k-NN, dimensionality reduction, classification algorithm
Keyword [sv]
EEG, MindWave, BCI, egenskapsvektor, förbehandling, k-NN, dimensionalitetsreducering, klassificeringsalgoritm
National Category
Media and Communication Technology
Identifiers
URN: urn:nbn:se:kth:diva-188798OAI: oai:DiVA.org:kth-188798DiVA: diva2:939115
Subject / course
Media Technology
Educational program
Master of Science in Engineering - Media Technology
Supervisors
Examiners
Available from: 2016-06-22 Created: 2016-06-17 Last updated: 2016-06-22Bibliographically approved

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Wilhelmsson_Oskar_och_Wikén_Victor-Genomförbarhetsstudie_av_att_känna_igen_två_tankemönster_i_följd_med_EEG(2690 kB)52 downloads
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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf