Literature DB >> 33721854

Towards real-life EEG applications: novel superporous hydrogel-based semi-dry EEG electrodes enabling automatically 'charge-discharge' electrolyte.

Guangli Li1, Sizhe Wang1,2, Mingzhe Li1,3, Yanwen Y Duan3.   

Abstract

Objective.A novel polyacrylamide/polyvinyl alcohol superporous hydrogel (PAAm/PVA SPH)-based semi-dry electrode was constructed for capturing electroencephalogram (EEG) signals at the hairy scalp, showing automatically 'charge-discharge' electrolyte concept in EEG electrode development.Approach.In this regard, PAAm/PVA SPH was polymerizedin-situin the hollow electrode cavity by freezing polymerization, which acted as a dynamic reservoir of electrolyte fluid. The SPH can be completely 'charged' with electrolyte fluid, such as saline, in just a few seconds and can be 'discharged' through a few tiny pillars into the scalp at a desirable rate. In this way, an ideal local skin hydration effect was achieved at electrode-skin contact sites, facilitating the bioelectrical signal pathway and significantly reducing electrode-skin impedance. Moreover, the electrode interface effectively avoids short circuit and inconvenient issues.Main results.The results show that the semi-dry electrode displayed low and stable contact impedance, showing non-polarization properties with low off-set potential and negligible potential drift. The average temporal cross-correlation coefficient between the semi-dry and conventional wet electrodes was 0.941. Frequency spectra also showed almost identical responses with anticipated neural electrophysiology responses.Significance.Considering prominent advantages such as a rapid setup, robust signal, and user-friendliness, the new concept of semi-dry electrodes shows excellent potential in emerging real-life EEG applications.
© 2021 IOP Publishing Ltd.

Entities:  

Keywords:  EEG signal; electrode impedance; semi-dry electrode; superporous hydrogel

Mesh:

Substances:

Year:  2021        PMID: 33721854     DOI: 10.1088/1741-2552/abeeab

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


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