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基于总体经验模态分解和独立成分分析的单通道失配负波记录中的人工耳蜗伪迹减少

Cochlear implant artifact reduction on one channel Mismatch Negativity recordings based on Ensemble Empirical Mode Decomposition and Independent Component Analysis.

作者信息

Martinez-Camacho Mauricio A, Castaneda-Villa N

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:6018-6021. doi: 10.1109/EMBC.2018.8513632.

DOI:10.1109/EMBC.2018.8513632
PMID:30441708
Abstract

Artifact generated by cochlear implants has been a problem for being able to register Mismatch Negativity (MMN) response. There are methods for reducing the artifact using multiple channels from the EEG but in this paper are presented the first results of a method using only the channel with the artifact using Ensemble Empirical Mode Decomposition (EEMD) and Independent Component Analysis (ICA). The first results showed that it was possible to get the MMN registers from the group of normal recordings and partially with the group of recordings from patients with cochlear implant. It is possible to suggest that EEMD in conjunction with ICA can be used for studies searching MMN.

摘要

人工耳蜗产生的伪迹一直是记录失配负波(MMN)反应的一个问题。有一些方法可以利用脑电图的多个通道来减少伪迹,但在本文中,展示了一种仅使用存在伪迹的通道,运用总体经验模态分解(EEMD)和独立成分分析(ICA)的方法的初步结果。初步结果表明,从正常记录组以及部分人工耳蜗患者的记录组中获取MMN记录是可能的。可以认为,EEMD与ICA相结合可用于搜索MMN的研究。

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