Permutation entropy: A new feature for brain-computer interfaces

Nicoletta Nicolaou, Julius Georgiou

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

9 Citations (Scopus)

Abstract

This paper investigates the use of Permutation Entropy (PE) as a feature for mental task classification for a Brain-Computer Interface system. PE is a recently introduced measure which quantifies signal complexity by measuring the departure of a time series from a random one. More regular signals are characterized by lower PE values. Here, PE is utilized to characterize signals from electroencephalograms of 3 subjects performing 4 motor imagery tasks, which are then classified using a Support Vector Machine. Even though it is possible to obtain 100% single-trial classification accuracy, this is very much subject-dependent.

Original languageEnglish
Title of host publication2010 IEEE Biomedical Circuits and Systems Conference, BioCAS 2010
Pages49-52
Number of pages4
DOIs
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event2010 IEEE Biomedical Circuits and Systems Conference, BioCAS 2010 - Paphos, Cyprus
Duration: 3 Nov 20105 Nov 2010

Conference

Conference2010 IEEE Biomedical Circuits and Systems Conference, BioCAS 2010
CountryCyprus
CityPaphos
Period3/11/105/11/10

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  • Cite this

    Nicolaou, N., & Georgiou, J. (2010). Permutation entropy: A new feature for brain-computer interfaces. In 2010 IEEE Biomedical Circuits and Systems Conference, BioCAS 2010 (pp. 49-52). [5709568] https://doi.org/10.1109/BIOCAS.2010.5709568