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ADVANCED TOPICS IN ELECTRICAL ENGINEERING – ATEE 2017

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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

 

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