Validation of the G.tec Unicorn Hybrid Black Wireless EEG System
Overview
Authors
Affiliations
Although dry and hybrid-style electrode technology has been well validated, systems utilizing these electrodes have not been widely adopted. One reason for this may be that the systems incorporating such technology present limitations that are fundamental to the EEG approach. The g.tec Unicorn Hybrid Black system, a low density Bluetooth EEG amplifier, however, attempts to address many of these limitations to allow greater flexibility to replicate methods used with traditional EEG amplifiers and extend them to more novel applications. The aim of the present investigation was to validate the g.tec Unicorn Hybrid Black amplifier to determine if it provides comparable data to a traditional laboratory-based system when no electrode preparation is utilized or if a saline-based solution is necessary to obtain sufficient signal quality. Stimulus-locked ERPs and EEG power spectrum data were concurrently recorded using both the Unicorn Hybrid Black amplifier and a traditional high-end laboratory-based low-impedance wired system. Findings suggest that the Unicorn Hybrid Black provides valid measures for investigations of frequency spectra even with no conductive solution applied. However, to obtain valid assessments of event-related brain potentials, it appears necessary to use a conductive solution for electrode preparation. This system appears well suited to allow for high-quality and flexible EEG measures available outside of traditional laboratory environments.
Image classification and reconstruction from low-density EEG.
Guenther S, Kosmyna N, Maes P Sci Rep. 2024; 14(1):16436.
PMID: 39013929 PMC: 11252274. DOI: 10.1038/s41598-024-66228-1.
Simultaneous Dry and Gel-Based High-Density Electroencephalography Recordings.
Fiedler P, Graichen U, Zimmer E, Haueisen J Sensors (Basel). 2023; 23(24).
PMID: 38139591 PMC: 10747542. DOI: 10.3390/s23249745.
Tamburro G, Fiedler P, De Fano A, Raeisi K, Khazaei M, Vaquero L Front Hum Neurosci. 2023; 17:1305331.
PMID: 38125713 PMC: 10730734. DOI: 10.3389/fnhum.2023.1305331.
Classifying human emotions in HRI: applying global optimization model to EEG brain signals.
Staffa M, DErrico L, Sansalone S, Alimardani M Front Neurorobot. 2023; 17:1191127.
PMID: 37881515 PMC: 10595007. DOI: 10.3389/fnbot.2023.1191127.
Samimisabet P, Krieger L, Nethar T, Pipa G Sensors (Basel). 2023; 23(17).
PMID: 37687895 PMC: 10490595. DOI: 10.3390/s23177440.