Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI

There are many factors outlined in the signal processing pipeline that impact brain-computer interface (BCI) performance, but some methodological factors do not depend on signal processing. Nevertheless, there is a lack of research assessing the effect of such factors. Here, we investigate the impact of VR, immersiveness, age, and spatial resolution on the classifier performance of a Motor Imagery (MI) electroencephalography (EEG)-based BO in naive participants. We found significantly better performance for VR compared to non-VR (15 electrodes: VR 77.48 +/- 6.09%, non-VR 73.5 +/- 5.89%, p = 0.0096; 12 electrodes: VR 73.26 +/- 5.2%, non-VR 70.87 +/- 4.96%, p = 0.0129; 7 electrodes: VR 66.74 +/- 5.92%, non-VR 63.09 +/- 8.16%, p = 0.0362) and better performance for higher electrode quantity, but no significant differences were found between immersive and nonimmersive VR. Finally, there was not a statistically significant correlation found between age and classifier performance, but there was a direct relation found between spatial resolution (electrode quantity) and classifier performance (r = 1, p = 0.0129, VR; r = 0.99, p = 0.0859, non-VR).