One century after the first recording of human electroencephalographic (EEG) signals, EEG has become one of the most used neuroimaging techniques. The medical devices industry is now able to produce small and reliable EEG systems, enabling a wide variety of applications also with no-clinical aims, providing a powerful tool to neuroscientific research. However, these systems still suffer from a critical limitation, consisting in the use of wet electrodes, that are uncomfortable and require expertise to install and time from the user. In this context, dozens of different concepts of EEG dry electrodes have been recently developed, and there is the common opinion that they are reaching traditional wet electrodes quality standards. However, although many papers have tried to validate them in terms of signal quality and usability, a comprehensive comparison of different dry electrode types from multiple points of view is still missing. The present work proposes a comparison of three different dry electrode types, selected among the main solutions at present, against wet electrodes, taking into account several aspects, both in terms of signal quality and usability. In particular, the three types consisted in gold-coated single pin, multiple pins and solid-gel electrodes. The results confirmed the great standards achieved by dry electrode industry, since it was possible to obtain results comparable to wet electrodes in terms of signals spectra and mental states classification, but at the same time drastically reducing the time of montage and enhancing the comfort. In particular, multiple-pins and solid-gel electrodes overcome gold-coated single-pin-based ones in terms of comfort.
|Titolo:||The dry revolution: Evaluation of three different eeg dry electrode types in terms of signal spectral features, mental states classification and usability|
DI FLUMERI, GIANLUCA (Corresponding author)
|Data di pubblicazione:||2019|
|Citazione:||The dry revolution: Evaluation of three different eeg dry electrode types in terms of signal spectral features, mental states classification and usability / Di Flumeri, G.; Arico, P.; Borghini, G.; Sciaraffa, N.; Di Florio, A.; Babiloni, F.. - In: SENSORS. - ISSN 1424-8220. - 19:6(2019).|
|Appare nella tipologia:||01a Articolo in rivista|