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 language | English |
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Title of host publication | 2010 IEEE Biomedical Circuits and Systems Conference, BioCAS 2010 |
Pages | 49-52 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 1 Dec 2010 |
Externally published | Yes |
Event | 2010 IEEE Biomedical Circuits and Systems Conference, BioCAS 2010 - Paphos, Cyprus Duration: 3 Nov 2010 → 5 Nov 2010 |
Conference
Conference | 2010 IEEE Biomedical Circuits and Systems Conference, BioCAS 2010 |
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Country/Territory | Cyprus |
City | Paphos |
Period | 3/11/10 → 5/11/10 |