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Features based on analytic IMF for classifying motor imagery EEG signals in BCI applications

Taran, S, Bajaj, V, Sharma, D, Siuly, Siuly ORCID: 0000-0003-2491-0546 and Sengur, A (2018) Features based on analytic IMF for classifying motor imagery EEG signals in BCI applications. Measurement, 116. pp. 68-76. ISSN 0263-2241

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Item Type: Article
Uncontrolled Keywords: brain-computer interface; intrinsic mode functions; empirical mode decomposition; electroencephalogram; least squares support vector machine; feature extraction; feature classification
Subjects: FOR Classification > 0801 Artificial Intelligence and Image Processing
FOR Classification > 0906 Electrical and Electronic Engineering
FOR Classification > 1702 Cognitive Science
Faculty/School/Research Centre/Department > Centre for Applied Informatics
Faculty/School/Research Centre/Department > College of Science and Engineering
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 23 May 2019 01:55
Last Modified: 27 May 2019 06:45
URI: http://vuir.vu.edu.au/id/eprint/38536
DOI: https://doi.org/10.1016/j.measurement.2017.10.067
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Citations in Scopus: 7 - View on Scopus

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