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Pattern Recognition Letters
Volume 22, Issue 12, October 2001, Pages 1299-1309
 
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doi:10.1016/S0167-8655(01)00075-7    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2001 Elsevier Science B.V. All rights reserved.

Hidden Markov models for online classification of single trial EEG data

B. ObermaierCorresponding Author Contact Information, E-mail The Corresponding Author, b, c, C. Gugera, C. Neuperb and G. Pfurtschellera, b

a Department of Medical Informatics, Institute for Biomedical Engineering, Graz University of Technology, Graz, Austria b Ludwig Boltzmann-Institute for Medical Informatics and Neuroinformatics, Graz University of Technology, Inffeldgasse 16a/II, A-8010, Graz, Austria c Instituto Superior Tecnico, ISR-LaSEEB, Lisbon, Portugal

Available online 25 July 2001.

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Abstract

Hidden Markov models (HMMs) are presented for the online classification of single trial EEG data during imagination of a left or right hand movement. The classification shows an improvement of the online experiment and the temporal determination of minimal classification error compared to linear classification methods.

Author Keywords: Brain-computer interface (BCI); Hidden Markov models; EEG classification; Event-related desynchronisation (ERD)

Article Outline

1. Introduction
2. Experimental setup and data acquisition
2.1. Subjects
2.2. Experimental procedure
2.3. EEG recordings
2.4. Data pre-processing
3. Bhattacharyya distance
4. Classification methods
4.1. The BCI–HMM system
5. Results
6. Discussion
Acknowledgements
References







Pattern Recognition Letters
Volume 22, Issue 12, October 2001, Pages 1299-1309
 
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