A Type-2 Fuzzy Approach Towards Cognitive Load Detection Using fNIRS Signals

Dan, M. and Saha, A. and Konar, A. and Ralescu, A. and Nagar, Atulya K. (2016) A Type-2 Fuzzy Approach Towards Cognitive Load Detection Using fNIRS Signals. In: 2016 IEEE International Conference on Fuzzy Systems, 24-29 July 2016, Vancouver, Canada.

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Abstract

The main notion of this paper is to identify the cognitive load during a mental arithmetic task experiment using fNIRS signals. The first objective is to classify the difficulty level and the state of inactivity during the given task. To identify the classes, the feature vectors have to undergo all the possible steps of a pattern classification problem. In this paper, we have developed a novel Feature Selection technique to reduce the dimension of the feature vectors by omitting the redundant features. For this purpose, an objective function depending upon the class density or likelihood functions is optimized using the well-known Differential Evolution algorithm. General type-2 fuzzy classifier is used for subsequent classification step. The proposed Feature selection technique gives a satisfactory accuracy results over principal component analysis. Also the fuzzy classifier outperforms the other well-known classifier like support vector machine, k-nearest neighborhood. The load of a subject undergoing the experiment is measured at a particular class relying upon the mean type- 1 fuzzy value of all feature entities.

Item Type: Conference or Workshop Item (Paper)
Additional Information and Comments: (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."
Keywords: Brain computer interfacing, functional near-infrared spectroscopy, fuzzy type-2 classifier, principal component analysis, differential evolution algorithm.
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Atulya Nagar
Date Deposited: 09 Jun 2016 11:45
Last Modified: 24 Nov 2016 11:57
URI: http://hira.hope.ac.uk/id/eprint/1442

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