A Boosting Approach to P300 Detection with Application to Brain-Computer Interfaces

U. Hoffmann, G. garcia, J. Vesin, K. Diserens, T. Ebrahimi
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引用次数: 100

Abstract

Gradient boosting is a machine learning method, that builds one strong classifier from many weak classifiers. In this work, an algorithm based on gradient boosting is presented, that detects event-related potentials in single electroencephalogram (EEG) trials. The algorithm is used to detect the P300 in the human EEG and to build a brain-computer interface (BCI), specifically a spelling device. Important features of the method described here are its high classification accuracy and its conceptual simplicity. The algorithm was tested with datasets recorded in our lab and one benchmark dataset from the BCI Competition 2003. The number of correctly inferred symbols with the P300 speller paradigm varied between 90% and 100%. In particular, all of the inferred symbols were correct for the BCI competition dataset
基于脑机接口的P300增强检测方法
梯度增强是一种机器学习方法,它从许多弱分类器中构建一个强分类器。在这项工作中,提出了一种基于梯度增强的算法来检测单次脑电图(EEG)试验中的事件相关电位。该算法被用于检测人类脑电图中的P300,并建立一个脑机接口(BCI),特别是一个拼写设备。本文描述的方法的重要特点是分类精度高,概念简单。该算法用我们实验室记录的数据集和2003年BCI竞赛的一个基准数据集进行了测试。使用P300拼写范式正确推断的符号数量在90%到100%之间变化。特别是,所有推断的符号对于BCI竞争数据集都是正确的
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