ITM Web Conf.
Volume 15, 2017II International Conference of Computational Methods in Engineering Science (CMES’17)
|Number of page(s)||7|
|Section||Computational And Artificial Intelligence|
|Published online||15 December 2017|
Classification of user performance in the Ruff Figural Fluency Test based on eye-tracking features
1 Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Institute of Computer Science, Nadbystrzycka 38D, 20-618 Lublin, Poland
2 Abdelmalek Essaâdi University, Polydisciplinary Faculty, Martil Road, Tétouan, Morocco
3 Medical University of Lublin, Department of Clinical Neuropsychiatry, Głuska 1, 20-439 Lublin, Poland
* Corresponding author: email@example.com
Cognitive assessment in neurological diseases represents a relevant topic due to its diagnostic significance in detecting disease, but also in assessing progress of the treatment. Computer-based tests provide objective and accurate cognitive skills and capacity measures. The Ruff Figural Fluency Test (RFFT) provides information about non-verbal capacity for initiation, planning, and divergent reasoning. The traditional paper form of the test was transformed into a computer application and examined. The RFFT was applied in an experiment performed among 70 male students to assess their cognitive performance in the laboratory environment. Each student was examined in three sequential series. Besides the students’ performances measured by using in app keylogging, the eye-tracking data obtained by non-invasive video-based oculography were gathered, from which several features were extracted. Eye-tracking features combined with performance measures (a total number of designs and/or error ratio) were applied in machine learning classification. Various classification algorithms were applied, and their accuracy, specificity, sensitivity and performance were compared.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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