ABSTRACT
Reading is central to learning and communicating, however, divided attention in the form of distraction may be present in learning environments, resulting in a limited understanding of the reading material. This paper presents a novel system that can spatio-temporally detect divided attention in users during two different reading applications: typical document reading and speed reading. Eye tracking and electroencephalography (EEG) monitor the user during reading and provide a classifier with data to decide the user's attention state. The multimodal data informs the system where the user was distracted spatially in the user interface and when the user was distracted. Classification was evaluated with two exploratory experiments. The first experiment was designed to divide the user's attention with a multitasking scenario. The second experiment was designed to divide the users attention by simulating a real-world scenario where the reader is interrupted by unpredictable audio distractions. Results from both experiments show that divided attention may be detected spatio-temporally well above chance on a single-trial basis.
- Emotiv EPOC. http://www.emotiv.com/.Google Scholar
- Spray Open Source Speed Reader. https://github.com/chaimpeck/spray/.Google Scholar
- Spritz. http://www.spritzinc.com/.Google Scholar
- The Eye Tribe Eye Tracker. http://www.theeyetribe.com/.Google Scholar
- Ang, K. K., Chin, Z. Y., Zhang, H., and Guan, C. Filter bank common spatial pattern (FBCSP) in brain-computer interface. In IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence) (June 2008), 2390--2397.Google Scholar
- Berka, C., Levendowski, D. J., Cvetinovic, M. M., Petrovic, M. M., Davis, G., Lumicao, M. N., Zivkovic, V. T., Popovic, M. V., and Olmstead, R. Real-time analysis of EEG indexes of alertness, cognition, and memory acquired with a wireless EEG headset. International Journal of Human-Computer Interaction 17, 2 (June 2004), 151--170.Google ScholarCross Ref
- Fernandes, M. A., and Moscovitch, M. Divided attention and memory: evidence of substantial interference effects at retrieval and encoding. Journal of Experimental Psychology. General 129, 2 (June 2000), 155--176.Google ScholarCross Ref
- Hamadicharef, B., Zhang, H., Guan, C., Wang, C., Phua, K. S., Tee, K. P., and Ang, K. K. Learning EEG-based spectral-spatial patterns for attention level measurement. In IEEE International Symposium on Circuits and Systems, 2009. ISCAS 2009 (May 2009), 1465--1468.Google ScholarCross Ref
- Klimesch, W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Research Reviews 29, 2--3 (Apr. 1999), 169--195.Google ScholarCross Ref
- Liu, N.-H., Chiang, C.-Y., and Chu, H.-C. Recognizing the degree of human attention using EEG signals from mobile. Sensors 13, 8 (Aug. 2013), 10273--10286.Google ScholarCross Ref
- Martínez-Gómez, P., and Aizawa, A. Recognition of understanding level and language skill using measurements of reading behavior. In Proceedings of the 19th International Conference on Intelligent User Interfaces, IUI '14, ACM (New York, NY, USA, 2014), 95--104. Google ScholarDigital Library
- Olsson, P. Real-time and offline filters for eye tracking. Master Thesis (2007).Google Scholar
- Putze, F., Hild, J., Kärgel, R., Herff, C., Redmann, A., Beyerer, J., and Schultz, T. Locating user attention using eye tracking and EEG for spatio-temporal event selection. In Proceedings of the 2013 International Conference on Intelligent User Interfaces, IUI '13, ACM (New York, NY, USA, 2013), 129--136. Google ScholarDigital Library
- Turk, M., and Robertson, G. Perceptual user interfaces (introduction). Commun. ACM 43, 3 (Mar. 2000), 32--34. Google ScholarDigital Library
- Yong, X., Fatourechi, M., Ward, R. K., and Birch, G. E. Automatic artefact removal in a self-paced hybrid braincomputer interface system. Journal of NeuroEngineering and Rehabilitation 9, 1 (July 2012), 50.Google ScholarCross Ref
Index Terms
- Spatio-Temporal Detection of Divided Attention in Reading Applications Using EEG and Eye Tracking
Recommendations
Classifying Attention Types with Thermal Imaging and Eye Tracking
Despite the importance of attention in user performance, current methods for attention classification do not allow to discriminate between different attention types. We propose a novel method that combines thermal imaging and eye tracking to ...
A real-time EEG-based BCI system for attention recognition in ubiquitous environment
UAAII '11: Proceedings of 2011 international workshop on Ubiquitous affective awareness and intelligent interactionSeveral types of biological signal, such as Electroencephalogram (EEG), electrooculogram(EOG), electrocardiogram(ECG), electromyogram (EMG), skin temperature variation and electrodermal activity, may be used to measure a human subject's attention level. ...
Locating user attention using eye tracking and EEG for spatio-temporal event selection
IUI '13: Proceedings of the 2013 international conference on Intelligent user interfacesIn expert video analysis, the selection of certain events in a continuous video stream is a frequently occurring operation, e.g., in surveillance applications. Due to the dynamic and rich visual input, the constantly high attention and the required hand-...
Comments