Performance analysis of ensemble methods for multi-class classification of motor imagery EEG signal

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

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

11 Citations (Scopus)
12 Downloads (Pure)

Abstract

Recent advances in the field of Brain-computer Interfacing (BCI) has opened wide potentials in neuro-rehabilitative applications. Electeroencephalography (EEG) is the most frequently used brain measure in BCI research. Mental states are distinguished from classifiers which uses features extracted from the raw EEG as inputs. Ensemble classifiers combine a number of classifiers or learners to improve the classification results. It is more suited for multi-class classification of time-varying EEG signal. In this paper, we have used AdaBoost, LPBoost, RUSBoost, Bagging and Random Subspaces for classification of 3-class motor imagery EEG data. For this purpose, we have employed adaptive autoregressive coefficients as features and feed forward neural network (FFNN) as the base learner of the ensemble methods. The results show that the classification accuracies of the ensemble classifiers except RUSBoost performs better than a single FFNN classifier.

Original languageEnglish
Title of host publicationInternational Conference on Control, Instrumentation, Energy and Communication, CIEC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages712-716
Number of pages5
ISBN (Electronic)9781479920440
DOIs
Publication statusPublished - 14 Nov 2014
Event2014 International Conference on Control, Instrumentation, Energy and Communication, CIEC 2014 - Kolkata, India
Duration: 31 Jan 20142 Feb 2014

Publication series

NameInternational Conference on Control, Instrumentation, Energy and Communication, CIEC 2014

Conference

Conference2014 International Conference on Control, Instrumentation, Energy and Communication, CIEC 2014
CountryIndia
CityKolkata
Period31/01/142/02/14

Keywords

  • Adaptive Autoregressive Parameter
  • Electroencephalography
  • Ensemble methods
  • Feed Forward Neural Network
  • Motor imagery
  • Multi-class classification

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