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An analysis of eye-tracking and electroencephalography data for cognitive load measurement during arithmetic tasks
The paper presents multiple features analysis of cognitive load case study. The set of features applied in the research covers response times, committed errors, EEG spectral data as well as pupillometry and eye-tracking (ET) data including fixations, saccades and blinks. The experiment took the form of eleven intervals: six containing arithmetic tasks and five breaks. Two correlation analyses were performed. The first one aimed in finding correlation between cognitive measure, EEG and ET features in each interval. The second analysis was performed to find correlation of cognitive workload and EEG and ET features. The results proved that the best cognitive workload measures are selected eye movement and pupil dilation measures.Author(s):
Magdalena BORYS
Lublin University of Technology, Institute of Computer Science
Poland
Mikhail TOKOVAROV
Lublin University of Technology, Institute of Computer Science
Poland
Martyna WAWRZYK
Lublin University of Technology, Institute of Computer Science
Poland
Kinga WESOLOWSKA
Lublin University of Technology, Institute of Computer Science
Poland
Malgorzata PLECHAWSKA-WOJCIK
Lublin University of Technology, Institute of Computer Science
Poland
Roman DMYTRUK
Lublin University of Technology, Institute of Computer Science
Poland
Monika KACZOROWSKA
Lublin University of Technology, Institute of Computer Science
Poland