基于独立分量分析的信道选择，实现N200和P300的高性能分类

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC) Pub Date : 2015-07-06 DOI:10.1109/ICCI-CC.2015.7259414

Wenxuan Li, Mengfan Li, Wei Li

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引用次数: 2

摘要

本文提出了一种实现高性能N200和P300分类的方法，该方法采用独立分量分析(ICA)方法选择脑信号中含有较大N200和P300电位和较小伪影的通道作为提取特征的最优通道。研究结果表明，我们的方法在4个受试者中平均准确率达到99.3%。

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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Independent component analysis-based channel selection to achieve high performance of N200 and P300 classification

This paper proposes a method for achieving a high performance of N200 and P300 classification, which applies independent component analysis (ICA) to select the channels whose brain signals contain large N200 and P300 potentials and small artifacts as the optimal channels to extract the features. The study results show that our method achieves an average accuracy of 99.3% over 4 subjects.

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来源期刊

2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)

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