Decoding of wrist and finger movement from electroencephalography signal

Monalisa Pal, Saugat Bhattacharyya, Amit Konar, D. N. Tibarewala, R. Janarthanan

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

6 Citations (Scopus)

Abstract

The emergence of brain-computer interfacing has made the control of robots through thought a reality. Such real-time application calls for fast processing and accurate classification of brain signals. In this paper, we address the two-level classification of motor imagery signals, where the user differentiates between clockwise/ counter-clockwise movement of wrist and the opening/closing of the fingers. For this purpose, parameters of adaptive autoregressive (AAR) models and Extreme Energy Ratio criterion (EER) are employed as features, which are fed to standard classifiers for comparison. It concludes the features extracted based on EER, selected by sequential forward search and classified using radial basis function kernelized support vector machine, provides optimum performance of the classification process for implementation in real-time scenario, with an average accuracy 90.24% and a time complexity of 8.2449 seconds.

Original languageEnglish
Title of host publicationIEEE CONECCT 2014 - 2014 IEEE International Conference on Electronics, Computing and Communication Technologies
PublisherIEEE Computer Society
ISBN (Print)9781479923175
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 IEEE International Conference on Electronics, Computing and Communication Technologies, IEEE CONECCT 2014 - Bangalore, India
Duration: 6 Jan 20147 Jan 2014

Publication series

NameIEEE CONECCT 2014 - 2014 IEEE International Conference on Electronics, Computing and Communication Technologies

Conference

Conference2014 IEEE International Conference on Electronics, Computing and Communication Technologies, IEEE CONECCT 2014
CountryIndia
CityBangalore
Period6/01/147/01/14

Keywords

  • Adaptive Auto-Regressive Model
  • Brain-Computer Interfacing
  • Distance Likelihood Ratio Test
  • Electroencephalography
  • Extreme Energy Ratio
  • Fisher Linear Discriminant
  • Naïve Bayes
  • Radial Basis Function based Support Vector Machine
  • Sequential Forward Feature Selection

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  • Cite this

    Pal, M., Bhattacharyya, S., Konar, A., Tibarewala, D. N., & Janarthanan, R. (2014). Decoding of wrist and finger movement from electroencephalography signal. In IEEE CONECCT 2014 - 2014 IEEE International Conference on Electronics, Computing and Communication Technologies [6740323] (IEEE CONECCT 2014 - 2014 IEEE International Conference on Electronics, Computing and Communication Technologies). IEEE Computer Society. https://doi.org/10.1109/CONECCT.2014.6740323