DISCRIMINATION OF TASK-RELATED EEG SIGNALS USING DIFFUSION ADAPTATION AND S-TRANSFORM COHERENCY

被引：0

作者：

Eftaxias, Konstantinos ^{[1
]}

Sanei, Saeid ^{[1
]}

机构：

[1] Univ Surrey, Fac Engn & Phys Sci, Guildford, Surrey, England

来源：

2014 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP) | 2014年

关键词：

Diffusion adaptation; brain connectivity; S-transform; diffusion Kalman filtering;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work presents a novel approach for discriminating complex mental and motor tasks using diffusion adaptation and brain connectivity measures. In particular, in this paper, we use a S-transform based measure to estimate the connectivity on single-trial basis and diffusion Kalman filtering to train a model that can classify different tasks. The superiority of the method is proven when compared with solutions that don't rely on cooperation.

引用

页数：6

共 41 条

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