Mind-Wandering Detection Model with Electroencephalogram [Konferenzbericht] Paper presented at the International Association for Development of the Information Society (IADIS) International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA) (18th, Virtual, Oct 13-15, 2021).

Autor/inn/en	Rungsilp, Chutimon; Piromsopa, Krerk; Viriyopase, Atthaphon; U-Yen, Kongpop
Titel	Mind-Wandering Detection Model with Electroencephalogram [Konferenzbericht] Paper presented at the International Association for Development of the Information Society (IADIS) International Conference on Cognition and Exploratory Learning in the Digital Age (CELDA) (18th, Virtual, Oct 13-15, 2021).
Quelle	(2021), (8 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Brain Hemisphere Functions; Diagnostic Tests; Artificial Intelligence; Cognitive Processes; Computer Software; Classification; Prediction; Usability; Accuracy; Models; Comparative Analysis; Foreign Countries; Thailand + Suchen Sie Ihr Suchwort? Diagnostic test; Diagnostischer Test; Künstliche Intelligenz; Cognitive process; Kognitiver Prozess; Classification system; Klassifikation; Klassifikationssystem; Vorhersage; Analogiemodell; Ausland
Abstract	The study of mind-wandering is popular since it is linked to the emotional problems and working/learning performance. In terms of education, it impacts comprehension during learning which affects academic success. Therefore, we sought to develop a machine learning model for an embedded portable device that can categorize mind-wandering state to assist people in keeping track of their minds. We utilize a low-channel EEG to record the brain state and to build the predictive model because of its practicality and user-friendly. Most machine learning experiments in mind-wandering using EEG exhibit good individual-level performance. For the group-level technique, only a few research has developed a model. As a result, the goal of this research is to achieve a high-accuracy group-level model. Thus, Leave One Participant Out Cross Validation (LOPOCV) was used to assess the model correctness. This study shows that using a baseline normalization technique assists feature extraction and improves performance. The model was built using a support vector machine (SVM), and the best model achieved an accuracy value of 75.6 percent. [For the full proceedings, see ED621108.] (As Provided).
Anmerkungen	International Association for the Development of the Information Society. e-mail: secretariat@iadis.org; Web site: http://www.iadisportal.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01