A High-Resolution Dry Electrode Array for SSVEP-Based Brain-Computer Interfaces

Zhiduo Liu, Yijun Wang, Weihua Pei, Xiao Xing, Qiang Gui, Hongda Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

This study aims to design a high-resolution dry electrode array, which can gather multi-channel Electroencephalogram (EEG) signals within a small scalp area. To investigate the independence of the multi-channel signals, the electrode array was applied to recording steady-state visual evoked potentials (SSVEPs) for a brain-computer interface (BCI) system. Currently, there is a certain contact area between the electrode and the scalp when gathering EEG signals. As a result, the acquired signal from one electrode might be a mixture of multiple components, which exhibit independent information, from the whole contact area. Therefore, a dry electrode array, which consists of multiple single-pin electrodes, might be more efficient to collect EEG signals with a spatial resolution at a millimeter scale. This study, therefore, designed a 16-channel high-resolution dry electrode array to record SSVEPs in a four-class BCI system. 16-channel EEG signals were acquired through the electrode array placed at the occipital area from four subjects. Through analyzing the relationship between the number of channels and the BCI performance, this study demonstrated that the electrode array can significantly improve the accuracy of SSVEP detection (12 channels: 88.5%, 1 channel: 80.9%, an average increase of 7.7%), verifying the independence of the SSVEP signals from a small area in the occipital region.

Original languageEnglish
Title of host publication9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PublisherIEEE Computer Society
Pages811-814
Number of pages4
ISBN (Electronic)9781538679210
DOIs
Publication statusPublished - 16 May 2019
Externally publishedYes
Event9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
Duration: 20 Mar 201923 Mar 2019

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2019-March
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Conference

Conference9th International IEEE EMBS Conference on Neural Engineering, NER 2019
Country/TerritoryUnited States
CitySan Francisco
Period20/03/1923/03/19

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