A differential evolution based adaptive neural Type-2 Fuzzy inference system for classification of motor imagery EEG signals

Debabrota Basu, Saugat Bhattacharyya, Dwaipayan Sardar, Amit Konar, D. N. Tibarewala, Atulya K. Nagar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
11 Downloads (Pure)

Abstract

This paper proposes a new classification algorithm which aims at predicting different states from an incoming non-stationary signal. To overcome the failure of standard classifiers at generalizing the patterns for such signals, we have proposed an Interval Type-2 Fuzzy based Adaptive neural fuzzy Inference System (ANFIS). Through the introduction IT2F system, we have aimed at improving the uncertainty management of the fuzzy inference system. Besides that using DE in forward and backward pass and improving the forward pass function we have improved the parameter update on wide range of nodal functions without any quadratic approximation in forward pass. The proposed algorithm is tested on a standard electroencephalography (EEG) dataset and it is noted that the proposed algorithm performs better than other standard classifiers including the classical ANFIS algorithm.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE
PublisherIEEE
Pages1253-1260
Number of pages8
ISBN (Electronic)9781479920723
DOIs
Publication statusPublished - 4 Sep 2014
Event2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2014 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014
CountryChina
CityBeijing
Period6/07/1411/07/14

Keywords

  • Adaptive Neural Fuzzy Inference
  • Brain-computer Interfacing
  • Differential Evolution
  • Electroencephalography
  • Interval Type-2 Fuzzy System

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