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Publication
Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI
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Advisor(s)
Abstract(s)
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 BCI in naïve 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 non immersive 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).
Description
Keywords
Motor imagery Brain-computer interfaces Virtual reality Classifier performance EEG . Faculdade de Ciências Exatas e da Engenharia
Citation
Blanco-Mora, D. A., Aldridge, A., Jorge, C., Vourvopoulos, A., Figueiredo, P., & Bermúdez i Badia, S. (2022). Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI. Brain-Computer Interfaces, 9 (3)..
Publisher
Taylor and Francis