Performance Analysis of Multiclass Common Spatial Patterns in Brain-Computer Interface

  • Soumyadip Chatterjee
  • Saugat Bhattacharyya
  • Amit Konar
  • D. N. Tibarewala
  • Anwesha Khasnobish
  • R. Janarthanan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)


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.


Brain-Computer Interfacing Electroencephalography Common Spatial Pattern Mutual Information Features Selection k-Nearest Neighbor Fuzzy k-Nearest Neighbor Linear Discriminant Analysis Quadratic Discriminant Analysis Support Vector Machine Nave-Bayesian 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Soumyadip Chatterjee
    • 1
  • Saugat Bhattacharyya
    • 1
  • Amit Konar
    • 1
  • D. N. Tibarewala
    • 2
  • Anwesha Khasnobish
    • 2
  • R. Janarthanan
    • 3
  1. 1.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia
  2. 2.School of Bioscience and EngineeringJadavpur UniversityKolkataIndia
  3. 3.Department of Computer ScienceTJS Engineering CollegeChennaiIndia

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