Paper Info
Title
A High-Resolution Dry Electrode Array For Ssvep-Based Brain-Computer Interfaces
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.
Year
DOI
Venue
2019
10.1109/ner.2019.8716951
2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)
Field
DocType
ISSN
Computer vision,Electrode array,Computer graphics (images),Computer science,Brain–computer interface,Artificial intelligence
Conference
1948-3546
Citations 
PageRank 
References 
0
0.34
0
Authors
6
Name
Order
Citations
PageRank
Zhiduo Liu1373.28
Yijun Wang230846.68
Weihua Pei36413.18
Xiao Xing400.34
Qiang Gui5173.10
Hongda Chen69920.06