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EEG signal classification using improved intuitionistic fuzzy twin support vector machines

  • S.I.: Improving Healthcare outcomes using Multimedia Big Data Analytics
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Abstract

Support-vector machines (SVMs) have been successfully employed to diagnose neurological disorders like epilepsy and sleep disorders via electroencephalogram (EEG) signal classification. However, EEG signals are susceptible to noise and outliers while recording. Thus, classification of EEG signals require efficient methods that is robust to noise and outliers. SVMs suffer in the presence of noise and outliers as it treats each sample to be of equal importance. Fuzzy membership function has been successful in dealing with noise and outliers. Fuzzy support vector machine uses fuzzy membership to give the appropriate weight to each sample for reducing the effect of noise and outliers. To reduce the effect of noise and outliers, intuitionistic fuzzy twin support vector machines (IFTWSVM) uses both membership and non-membership weights to subsidise the effect of outliers. Moreover, IFTWSVM solves smaller size quadratic programming problems which makes it comparatively efficient than fuzzy support vector machines. However, IFTWSVM suffers as (i) it involves the computation of matrix inverses which makes it intractable as the size of the data increases. (ii) it solves different problems for linear and nonlinear cases, and nonlinear case with linear kernel does not reduce to the linear case. Thus, it can only solve the approximate formulation not the exact formulation. To overcome these issues, we propose improved intuitionistic fuzzy twin support vector machine (IIFTWSVM). The proposed IIFTWSVM introduces different Lagrangian functions to avoid the computation of matrix inverses. Also, in nonlinear case, kernel trick is applied directly and hence, the exact formulation is solved. We employed the proposed IIFTWSVM for the classification of EEG signals. The experimental results and statistical analysis show that the proposed IIFTWSVM model is better compared to the given baseline models. Also, we evaluate the performance of the models on KEEL datasets to check the robustness of the proposed IIFTWSVM model.

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Acknowledgements

This work is funded by Government of India, Science and Engineering Research Board (SERB) under Ramanujan Fellowship Scheme, Grant No. SB/S2/RJN-001/2016, Department of Science and Technology under Interdisciplinary Cyber Physical Systems (ICPS) Scheme grant no. DST/ICPS/CPS-Individual/2018/276 and National Supercomputing Mission under DST and Miety, Govt. of India under Grant No. DST/NSM/R &D_HPC_Appl/2021/03.29. We acknowledge Council of Scientific & Industrial Research (CSIR), New Delhi, INDIA for Extra Mural Research (EMR) grant, under grant no. 22(0751)/17/EMR-II. Ms. Anuradha Kumari (File no - 09/1022 (12437)/2021-EMR-I) and Mr. Ashwani kumar Malik (File no - 09/1022 (0075)/2019-EMR-I) acknowledge the financial support given as scholarship by Council of Scientific and Industrial Research (CSIR), New Delhi, India. We are grateful for the facilities and support given by Indian Institute of Technology Indore, India.

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  1. Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, 453552, India

    M. A. Ganaie, Anuradha Kumari, A. K. Malik & M. Tanveer

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  1. M. A. Ganaie
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Ganaie, M.A., Kumari, A., Malik, A.K. et al. EEG signal classification using improved intuitionistic fuzzy twin support vector machines. Neural Comput & Applic 36, 163–179 (2024). https://doi.org/10.1007/s00521-022-07655-x

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