Multiclass Classification of EEG Signal Using a Probabilistic Approach

被引：2

作者：

Venate, Salini ^{[1
]}

Sunny, T. D. ^{[1
]}

机构：

[1] GEC Thrissur, Trichur, Kerala, India

来源：

INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING, SCIENCE AND TECHNOLOGY (ICETEST - 2015) | 2016年 / 24卷

关键词：

Brain ComputerInterfacing; Common satial pattern; Multi-class;

D O I：

10.1016/j.protcy.2016.05.219

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Brain Computer Interfacing (BCI) also called Brain Machine Interfacing (BMI)) is a challenging problem that forms part of a larger research area, called the Human Computer Interfacing (HCI), which interlinks thoughts to action. In BCI systems, the user messages or commands do not depend on the normal output channels of the brain. Therefore the main objective of BCI is to process the electrical signals generated by the neurons in the brain and generate the necessary signals to control some external systems. This paper investigates the feasibility of using Bayesian Spatio Spectral Filter Optimization algorithm for motor imagery classification in a multiclass scenario. (C) 2016 The Authors. Published by Elsevier Ltd.

引用

页码：1002 / 1007

页数：6

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