Hurst Exponent as a New Ingredient to Parametric Feature Set for Mental Task Classification
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Electroencephalograph (EEG) is a popular modality to capture signals associated with brain activities in a given time window. One of the powerful applications of EEG signal is in developing Brain–Computer Interface (BCI) systems. Response to mental tasks is one of BCI systems which helps disabled persons to communicate their need to the machines through signals related to particular thought also known as Mental Task Classification (MTC). The success of application depends on the efficient analysis of these signals for further classification. Empirical Mode Decomposition (EMD), a filter-based heuristic technique, is utilized to analyze EEG signal in the recent past. In this work, feature extraction from the EEG signal is done in two stages. In the first stage, the signal is broken into a number of oscillatory functions by means of EMD algorithm. The second stage involves compact representation in terms of eight different statistics (features) obtained from each function. Hurst Exponent as a new ingredient to parametric feature set is investigated to check its suitability for MTC. Support Vector Machine (SVM) classifier is utilized to develop a classification model and to validate the proposed approach for feature construction for classifying the different mental tasks. Experimental result on a publicly available dataset shows the superior performance of the proposed approach in comparison to the state-of-the-art methods.
KeywordsBrain–computer interface Response to mental tasks Feature extraction Empirical mode decomposition Electroencephalograph
The authors express their gratitude to Cognitive Science Research Initiative (CSRI), DST and DBT, Govt. of India, and CSIR, India for obtained research grant.
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