About: An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data
Creator	Seo, Jungryul Sohn, Kyung-Ah Laine, Teemu
Source	PMC
abstract	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.
has issue date	2019-10-20(xsd:dateTime)
bibo:doi	10.3390/s19204561
bibo:pmid	31635194
has license	cc-by
sha1sum (hex)	88511032b2c7ac3a42adf31909cb0c281c079922
schema:url	https://doi.org/10.3390/s19204561
resource representing a document's title	An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data
has PubMed Central identifier	PMC6832442
has PubMed identifier	31635194
schema:publication	Sensors (Basel)
resource representing a document's body	covid:88511032b2c7ac3a42adf31909cb0c281c079922#body_text
is schema:about of	named entity 'BOREDOM' named entity 'MACHINE LEARNING' named entity 'data' named entity 'EEG' named entity 'collected' named entity 'emotions' named entity 'boredom' named entity 'Boredom' named entity 'Exploration' named entity 'SELECTED' named entity 'DATA ACQUISITION' named entity 'RESULTS' named entity 'QUESTIONNAIRE' named entity 'TO DETECT' named entity 'FEATURES' named entity 'POTENTIAL' named entity 'BEST' named entity 'USEFUL' named entity 'PERCEPTRON' named entity 'RESEARCHED' named entity 'COMPUTING' named entity 'EEG' named entity 'FOLD' named entity 'COMBINED EFFECT' named entity 'TESTIMONIES' named entity 'CANDIDATE' named entity 'SET' named entity 'CLASSIFICATION' named entity 'TEST RESULTS' named entity 'MULTILAYER' named entity 'SAMPLES' named entity 'REVEALED' named entity 'CHALLENGES' named entity 'ISSUES' named entity 'COLLECTED' named entity 'HAVE' named entity 'VARIOUS' named entity 'YEARS' named entity 'TRAINED' named entity 'PREVIOUS' named entity '1000' named entity 'RECENT' named entity 'ACCURACY' named entity 'MEAN' named entity 'EMOTIONS' named entity 'COMPUTING SYSTEMS' named entity 'BUILDING' named entity 'MEDICAL' named entity 'DATA' named entity 'AWARENESS' named entity 'ELICIT' named entity 'VALIDATED' named entity 'CLIP' named entity 'USER' named entity 'DAILY' named entity 'STUDIES' named entity 'ROBUST' named entity 'FINAL' named entity 'BUT' named entity 'GALVANIC SKIN RESPONSE' named entity 'MODELS' named entity 'EEG' named entity '27S' named entity 'STIMULI' named entity 'TO INVESTIGATE' named entity 'EXPLORATION' named entity 'GSR' named entity 'METHODS' named entity 'DATA' named entity 'PARTICULAR' named entity 'BOREDOM' named entity 'SENSORS' named entity 'GSR' named entity 'MODEL' named entity 'LIFE' named entity 'TO PROVIDE' named entity 'BASED' named entity 'KNOWLEDGE' named entity 'EMOTION'

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