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.
|Title of host publication||2010 IEEE Biomedical Circuits and Systems Conference, BioCAS 2010|
|Number of pages||4|
|Publication status||Published - 1 Dec 2010|
|Event||2010 IEEE Biomedical Circuits and Systems Conference, BioCAS 2010 - Paphos, Cyprus|
Duration: 3 Nov 2010 → 5 Nov 2010
|Conference||2010 IEEE Biomedical Circuits and Systems Conference, BioCAS 2010|
|Period||3/11/10 → 5/11/10|