Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces
Convolutional Neural Networks for P300 Detection with Application to Brain-Computer Interfaces
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DOI：
10.1109/tpami.2010.125
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发表时间：
2011-03-01
影响因子：
23.6
通讯作者：
Graeser, Axel
中科院分区：
文献类型：
--
作者：
Cecotti, Hubert;Graeser, Axel
脑-机接口（BCI）是一种特定类型的人机接口，通过分析大脑测量值实现人与计算机之间的直接通信。在BCI中使用Oddball范例来在用户选择的目标上生成事件相关电位（ERP），如P300波。P300拼写器基于这一原理，其中P300波的检测允许用户书写字符。P300拼写器由两个分类问题组成。第一种分类是检测脑电图（EEG）中P300的存在。第二个对应于不同P300反应的组合，用于确定要拼写的正确字符。提出了一种新的P300波检测方法。该模型基于卷积神经网络（CNN）。网络的拓扑结构适于在时域中检测P300波。提出了七个基于CNN的分类器：四个具有不同特征集的单分类器和三个多分类器。在第三届BCI竞赛的数据集II上对这些模型进行了测试和比较。最好的结果是获得了一个多分类器的解决方案，识别率为95.5%，没有通道选择之前的分类。由于CNN模型的感受野，所提出的方法也为分析大脑活动提供了一种新的方法。
A Brain-Computer Interface (BCI) is a specific type of human-computer interface that enables the direct communication between human and computers by analyzing brain measurements. Oddball paradigms are used in BCI to generate event-related potentials (ERPs), like the P300 wave, on targets selected by the user. A P300 speller is based on this principle, where the detection of P300 waves allows the user to write characters. The P300 speller is composed of two classification problems. The first classification is to detect the presence of a P300 in the electroencephalogram ( EEG). The second one corresponds to the combination of different P300 responses for determining the right character to spell. A new method for the detection of P300 waves is presented. This model is based on a convolutional neural network (CNN). The topology of the network is adapted to the detection of P300 waves in the time domain. Seven classifiers based on the CNN are proposed: four single classifiers with different features set and three multiclassifiers. These models are tested and compared on the Data set II of the third BCI competition. The best result is obtained with a multiclassifier solution with a recognition rate of 95.5 percent, without channel selection before the classification. The proposed approach provides also a new way for analyzing brain activities due to the receptive field of the CNN models.