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挠性印刷额部 EEG 传感器(fEEGrid)用于长期 EEG 采集。

Flex-printed forehead EEG sensors (fEEGrid) for long-term EEG acquisition.

机构信息

Department of Psychology, Carl von Ossietzky University of Oldenburg, Germany. Author to whom any correspondence should be addressed.

出版信息

J Neural Eng. 2020 Jun 22;17(3):034003. doi: 10.1088/1741-2552/ab914c.

Abstract

OBJECTIVE

In this report we present the fEEGrid, an electrode array applied to the forehead that allows convenient long-term recordings of electroencephalography (EEG) signals over many hours.

APPROACH

Twenty young, healthy participants wore the fEEGrid and completed traditional EEG paradigms in two sessions on the same day. The sessions were eight hours apart, participants performed the same tasks in an early and a late session. For the late session fEEGrid data were concurrently recorded with traditional cap EEG data.

MAIN RESULTS

Our analyses show that typical event-related potentials responses were captured reliably by the fEEGrid. Single-trial analyses revealed that classification was possible above chance level for auditory and tactile oddball paradigms. We also found that the signal quality remained high and impedances did not deteriorate, but instead improved over the course of the day. Regarding wearing comfort, all participants indicated that the fEEGrid was comfortable to wear and did not cause any pain even after 8 h of wearing it.

SIGNIFICANCE

We show in this report, that high quality EEG signals can be captured with the fEEGrid reliably, even in long-term recording scenarios and with a signal quality that may be considered suitable for online brain-computer Interface applications.

摘要

目的

在本报告中,我们介绍了 fEEGrid,这是一种应用于前额的电极阵列,可方便地长时间记录脑电图 (EEG) 信号数小时。

方法

二十名年轻、健康的参与者佩戴 fEEGrid,并在同一天的两次会议上完成传统 EEG 范式。两次会议相隔八小时,参与者在早期和晚期会议上执行相同的任务。对于晚期会议,fEEGrid 数据与传统帽式 EEG 数据同时记录。

主要结果

我们的分析表明,fEEGrid 可靠地捕获了典型的事件相关电位反应。单次试验分析表明,听觉和触觉异常范式的分类高于偶然水平。我们还发现,信号质量保持较高,阻抗没有恶化,反而随着时间的推移而改善。关于佩戴舒适度,所有参与者都表示 fEEGrid 佩戴舒适,即使佩戴 8 小时后也不会引起任何疼痛。

意义

我们在本报告中表明,即使在长时间记录的情况下,fEEGrid 也可以可靠地捕获高质量的 EEG 信号,并且信号质量可能适合在线脑机接口应用。

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