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On the Temporal Behavior of EEG Recorded during Real Finger Movement

  • Conference paper
Machine Learning and Data Mining in Pattern Recognition (MLDM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6871))

Abstract

This paper studies the temporal behavior of EEG data during self-paced real finger movement using Hidden Markov Models and Conditional Random Fields and proposes novel temporal classification methods for movement classification versus idle state. Results are compared to those from Linear Discriminant Analysis based classification. It is demonstrated that using the temporal information in the classification model itself can significantly improve the performance of self-paced EEG classification. The proposed methods are tested on 15 subjects, achieving between 57% and 88% cross validation accuracy, with an average 6% improvement in classification accuracy.

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Author information

Authors and Affiliations

  1. School of Medical Sciences, University of Aberdeen, IMS Building, Foresterhill, Aberdeen, AB25 2ZD, UK

    Bashar Awwad Shiekh Hasan

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  1. Bashar Awwad Shiekh Hasan
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Editor information

Editors and Affiliations

  1. Intitute of Computer Vision and Applied Computer Sciences, IBaI, Kohlenstraße 2, 04107, Leipzig, Germany

    Petra Perner

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Hasan, B.A.S. (2011). On the Temporal Behavior of EEG Recorded during Real Finger Movement. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_25

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  • DOI: https://doi.org/10.1007/978-3-642-23199-5_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23198-8

  • Online ISBN: 978-3-642-23199-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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