Classification of BCI Users Based on Cognition
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-05-09
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Brain-Computer Interfaces (BCI) are systems originally developed to assist paralyzed patients allowing for commands to the computer with brain activities.
This study aims to examine cognitive state with an objective, easy-to-use, and easy-to-interpret method utilizing Brain-Computer Interface systems.
Seventy healthy participants completed six tasks using a Brain-Computer Interface system and participants’ pupil dilation, blink rate, and Galvanic Skin Response (GSR) data were collected simultaneously.
Participants filled Nasa-TLX forms following each task and task performances of participants were also measured.
Cognitive state clusters were created from the data collected using the K-means method.
Taking these clusters and task performances into account, the general cognitive state of each participant was classified as low risk or high risk.
Logistic Regression, Decision Tree, and Neural Networks were also used to classify the same data in order to measure the consistency of this classification with other techniques and the method provided a consistency between 87.1% and 100% with other techniques.
American Psychological Association (APA)
Ozkan, N. Firat& Kahya, Emin. 2018. Classification of BCI Users Based on Cognition. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130798
Modern Language Association (MLA)
Ozkan, N. Firat& Kahya, Emin. Classification of BCI Users Based on Cognition. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1130798
American Medical Association (AMA)
Ozkan, N. Firat& Kahya, Emin. Classification of BCI Users Based on Cognition. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130798
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130798