Permutation Entropy: a new feature for Brain-Computer Interfaces

被引：7

作者：

Nicolaou, Nicoletta ^{[1
]}

Georgiou, Julius ^{[1
]}

机构：

[1] Univ Cyprus, Dept Elect & Comp Engn, KIOS, Holist Elect Res Lab, Kallipoleos 75, CY-1678 Nicosia, Cyprus

来源：

2010 BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS) | 2010年

关键词：

BCI;

D O I：

10.1109/BIOCAS.2010.5709568

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

收藏

页码：49 / 52

页数：4

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