基于神经网络分类器的脑电图字母拼写器P300信号检测

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI:10.1109/NEUREL.2018.8587033

G. Tsenov, V. Mladenov

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引用次数: 1

摘要

本文介绍了一个P300字母拼写学习案例的实现。该拼字器基于将字母符号实时显示在屏幕上的方法，通过实时脑电图测量人类脑电波，通过P300事件诱发电位信号检测。在MATLAB中使用Emotiv Epoc EEG+耳机实现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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EEG alphabet speller with Neural Network classifier for P300 signal detection

This paper presents the implementation of a P300 alphabet speller study case. The speller is based on methodology in which alphabet symbols are displayed in real-time on screen to test subjects, with real-time electroencephalograph measurement of human brainwaves, with P300 event evoked potential signal detection. The implementation is done in MATLAB with Emotiv Epoc EEG+ headset.

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2018 14th Symposium on Neural Networks and Applications (NEUREL)

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