A type-2 fuzzy approach towards cognitive load detection using fNIRS signals

Abstract

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.Keywords-Brain computer interfacing, functional nearinfrared spectroscopy, fuzzy type-2 classifier, principal component analysis, differential evolution algorithm.

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