Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data
Department of Computer Engineering, Ajou University, Suwon, Korea.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.ORCID-id: 0000-0001-5966-992x
Department of Computer Engineering, Ajou University, Suwon, Korea.
2019 (engelsk)Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, nr 20, artikkel-id 4561Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted to detect the user’s emotions from physiological data. Among a myriad of target emotions, boredom, in particular, has been suggested to cause not only medical issues but also challenges in various facets of daily life. However, to the best of our knowledge, no previous studies have used electroencephalography (EEG) and galvanic skin response (GSR) together for boredom classification, although these data have potential features for emotion classification. To investigate the combined effect of these features on boredom classification, we collected EEG and GSR data from 28 participants using off-the-shelf sensors. During data acquisition, we used a set of stimuli comprising a video clip designed to elicit boredom and two other video clips of entertaining content. The collected samples were labeled based on the participants’ questionnaire-based testimonies on experienced boredom levels. Using the collected data, we initially trained 30 models with 19 machine learning algorithms and selected the top three candidate classifiers. After tuning the hyperparameters, we validated the final models through 1000 iterations of 10-fold cross validation to increase the robustness of the test results. Our results indicated that a Multilayer Perceptron model performed the best with a mean accuracy of 79.98% (AUC: 0.781). It also revealed the correlation between boredom and the combined features of EEG and GSR. These results can be useful for building accurate affective computing systems and understanding the physiological properties of boredom.

sted, utgiver, år, opplag, sider
MDPI, 2019. Vol. 19, nr 20, artikkel-id 4561
Emneord [en]
boredom, machine learning, emotion, EEG, GSR, classification, sensor
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-76452DOI: 10.3390/s19204561PubMedID: 31635194Scopus ID: 2-s2.0-85073657062OAI: oai:DiVA.org:ltu-76452DiVA, id: diva2:1362495
Merknad

Validerad;2019;Nivå 2;2019-10-21 (johcin)

Tilgjengelig fra: 2019-10-21 Laget: 2019-10-21 Sist oppdatert: 2019-11-04bibliografisk kontrollert

Open Access i DiVA

fulltext(4409 kB)5 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 4409 kBChecksum SHA-512
1ab2520897340010785619a0c3141e049b2fe15f92140553bceec82433a611e3c08487266790d228599dfe842376d43ce0f2c9a7b583c01c0c4a17ce5c477f8c
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekstPubMedScopus

Personposter BETA

Laine, Teemu H.

Søk i DiVA

Av forfatter/redaktør
Laine, Teemu H.
Av organisasjonen
I samme tidsskrift
Sensors

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 5 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
pubmed
urn-nbn

Altmetric

doi
pubmed
urn-nbn
Totalt: 2 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf