Classifying cognitive workload based on brain waves signal in the arithmetic tasks' study
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
20
Poziom I
| Status: | |
| Autorzy: | Plechawska-Wójcik Małgorzata, Borys Magdalena, Tokovarov Mikhail, Kaczorowska Monika, Wesołowska Kinga, Wawrzyk Martyna |
| Dyscypliny: | |
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| Wersja dokumentu: | Drukowana | Elektroniczna |
| Arkusze wydawnicze: | 0,5 |
| Język: | angielski |
| Strony: | 277 - 283 |
| Web of Science® Times Cited: | 6 |
| Scopus® Cytowania: | 8 |
| Bazy: | Web of Science | Scopus | IEEE |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | 11th International Conference on Human System Interaction |
| Skrócona nazwa konferencji: | HSI 2018 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 4 lipca 2018 do 6 lipca 2018 |
| Miasto konferencji: | Gdańsk |
| Państwo konferencji: | POLSKA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| Cognitive workload is a quantitative usage measure of the limited amount of working memory. Its measuring is of great importance for understanding human mental effort processing, evaluating information systems or supporting diagnosis and treatment of patients. The paper presents the results of cognitive workload classification of electroencephalographic (EEG) data. The performed study covered arithmetic tasks realised in several intervals with the increasing difficulty level. Brain waves data in the form of EEG signal were gathered and processed in the form of frequency spectra. The paper discusses the process of features selection performed with several methods including ranking methods (K-Fisher), Feature Selection By Eigenvector Centrality (ECFS) and Mitinffs mutual information-based approach. What is more, the paper presents results of participant cognitive workload classification based on such methods as Support Vector Machines (SVM), boosted trees and k-nearest neighbours (KNN) algorithm. The paper discusses the efficiency of features selection methods and accuracy of applied classification methods. |
