Consumer-grade smart-glasses are available now and are being increasingly used in Visually Evoked Potential - Brain Computer Interfaces (VEP-BCI) applications. Among various paradigms, these application represents the one called reactive BCI, in which the user is exposed to a visual stimulus from a display (the smart glasses lenses in this case) and the evoked brain response is detected usually by means of an electroencelograph (EEG) 
For example, in steady state VEP-BCI, when two icons, blinking at different frequency (e.g. 10 Hz and 12 Hz), are shown to the user, the EEG measurement of the evoked brain potential allow to discriminate the user attention for one rather that the other icon, without ani active action required by the user. Such a VEP-BCI application requires high distinguishability of the elicited brain potentials to be reliable [2 § 3.2.2], and this aspect may strongly depend on the visual stimulus actually induced by the optical output of the smart glasses.
We characterised the optical output of three models of smart glasses with different display technology, i) Epson BT-200 (based on LCD technology), ii) Epson BT-350 (OLED) and iii) Microsoft Hololens (waveguides), by means of a photo-transducer based on a OPT-101 photodiode, in order to get insight on the exploit-ability of these smart glasses in VEP-BCI applications .
Results will be presented at the conference showing that the display technology used in different models of these consumer-grade smart glasses, and other characteristics, lead to rather different optical outputs for the same nominal programmed stimulus. Hence the choice of the visual technology may strongly depend on the particular target application.
 Arpaia P, Callegaro L, Cultrera A, Esposito A, Ortolano M. Metrological characterization of a low-cost electroencephalograph for wearable neural interfaces in industry 4.0 applications. In2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0 & IoT) 2021 Jun 7 (pp. 1-5). IEEE.
 Abiri R, Borhani S, Sellers EW, Jiang Y, Zhao X. A comprehensive review of EEG-based brain–computer interface paradigms. Journal of neural engineering. 2019 Jan 9; 16(1) : 011001.