Performance analysis of multiclass common spatial patterns in brain-computer interface

Soumyadip Chatterjee, Saugat Bhattacharyya, Amit Konar, D. N. Tibarewala, Anwesha Khasnobish, R. Janarthanan

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

Abstract

Brain-Computer Interfacing (BCI) aims to assist, enhance, or repair human cognitive or sensory-motor functions. The classification of EEG signals plays a crucial role in BCI implementation. In this paper we have implemented a multi-class CSP Mutual Information Feature Selection (MIFS) algorithm to classify our EEG data for three class Motor Imagery BCI and have presented a comparative study of different classification algorithms including k-nearest neighbor (kNN) and Fuzzy kNN algorithm, linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA), support vector machine (SVM), radial basis function (RBF) SVM and Naive Bayesian (NB) classifiers algorithms. It is observed that Fuzzy kNN and kNN algorithm provides the highest classification accuracy of 92.65% and 92.29% which surpasses the classification accuracy of the other algorithms.

Original languageEnglish
Title of host publicationPattern Recognition and Machine Intelligence - 5th International Conference, PReMI 2013, Proceedings
Pages115-120
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2013
Event5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013 - Kolkata, India
Duration: 10 Dec 201314 Dec 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8251 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013
CountryIndia
CityKolkata
Period10/12/1314/12/13

Keywords

  • Brain-Computer interfacing
  • Common spatial pattern
  • Electroencephalography
  • Fuzzy k-nearest neighbor
  • k-Nearest neighbor
  • Linear discriminant analysis
  • Mutual information features selection
  • Nave-Bayesian
  • Quadratic discriminant analysis
  • Support vector machine

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  • Cite this

    Chatterjee, S., Bhattacharyya, S., Konar, A., Tibarewala, D. N., Khasnobish, A., & Janarthanan, R. (2013). Performance analysis of multiclass common spatial patterns in brain-computer interface. In Pattern Recognition and Machine Intelligence - 5th International Conference, PReMI 2013, Proceedings (pp. 115-120). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8251 LNCS). https://doi.org/10.1007/978-3-642-45062-4_15