Wearable neurotechnologies that perform electroencephalography (EEG), non-invasively, present several challenges. Specifically, a characteristic low signal-to-noise ratio (SNR) due to artifacts, non-stationary effects, and inter-subject (user) variability, are problematic. Consequently, there is a need to maximise signal quality and robustness. These criteria were at the fore during the development of the FlexEEG system, which is a lowcost, unobtrusive, wearable neurotechnology built on a flexible printed circuit board (PCB) substrate that folds around the head and is concealable in standard headwear. The aim of this paper is to compare the flexEEG system and a medical grade EEG system, through empirically testing the respective performances of each on a simulated dataset, as well as within the framework of a motor-imagery brain-computer interface (BCI) paradigm, and a motion onset visual evoked potentials (mVEP) BCI paradigm. Three healthy participants completed two training sessions on separate days, using both the FlexEEG (NeuroCONCISE) and the medical grade EEG headset, to evaluate performance on a 2-class motor-imagery task and 5 class mVEP task. Decoding accuracies are compared across training sessions for each headset. The EEG system for the comparison is a high-performance non-invasive medical product for use within research and clinical environments. The signal quality of EEG produced by each headset is also compared by assessing in terms of power spectral density of simulated sinusoidal data at 2 Hz, 8 Hz, 16 Hz and 25 Hz. Decoding accuracies are compared across training sessions for each headset. Insignificant differences were observed between performances of both headsets regarding signal quality and functional capability, suggesting the flexEEG offers potential as a reliable, low-cost wearable neurotechnology for EEG research and end-use applications.
|Title of host publication||2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)|
|Number of pages||6|
|ISBN (Electronic)||978-1-6654-8574-6, 978-1-6654-8573-9, 978-1-6654-8575-3|
|Publication status||Published online - 5 Dec 2022|
|Event||IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) - Rome, Italy|
Duration: 26 Oct 2022 → 28 Oct 2022
|Name||2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings|
|Conference||IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)|
|Period||26/10/22 → 28/10/22|
Bibliographical noteFunding Information:
Neurotechnology Innovation Hub, funded by The Department for the Economy, Northern Ireland.
© 2022 IEEE.
- Motor Imagery