Dynamic stopping in P300 speller with convolutional neural network
Dynamic stopping in P300 speller with convolutional neural network
复制标题
使用卷积神经网络在 P300 拼写器中动态停止
DOI：
10.1109/ner.2017.8008370
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Xichun Zhang
中科院分区：
文献类型：
--
作者：
Zhubing Chen;Xichun Zhang
在 P300 拼写器脑机接口 (BCI) 中，刺激序列会多次呈现给受试者，以实现可靠的 P300 检测。传统上，轮数是固定的且相对较大（例如2005年BCI竞赛的Wadsworth数据集中为15轮），这导致信息传输率较低。为了提高字符识别速度而不影响拼写准确性，我们建议使用卷积神经网络（CNN）进行动态停止。与传统的静态停止准则（SSC）相比，我们的方法可以有效提高系统的信息传输率。
In P300 speller brain-computer interface (BCI), the stimulus sequence is presented to subject for several rounds to achieve reliable P300 detection. Traditionally, the number of rounds is fixed and relatively large (e.g., 15 in the Wadsworth Dataset of BCI Competition 2005), which results in low information transfer rate. In order to improve the speed of character recognition without affecting the spelling accuracy, we propose to use convolutional neural network (CNN) into the dynamic stopping. Compared with the traditional static stopping criterion (SSC), our method can effectively improve the information transfer rate of the system.