Neuroergonomic Comparison of User Experience and System Performance for SSVEP-Based Brain Computer Interfaces in Mixed Reality and Virtual Reality Environments

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) deliver high classification accuracy and information transfer rates (ITR), yet their neural responses and user experience vary across immersive display environments. This study compares SSVEP signal characteristics, offline decoding performance, and subjective experience between mixed reality (MR) and virtual reality (VR) head-mounted display (HMD) setups to inform ecologically valid real-world BCI paradigms. Identical visual flicker stimuli (10, 11.25, 15, 18 Hz) were presented via Apple Vision Pro under MR and VR modes. Nine occipito - parietal electroencephalogram (EEG) channels were processed with filter bank canonical correlation analysis (FBCCA) to calculate signal-to-noise ratio (SNR), offline classification accuracy (ACC), and ITR. Subjective workload and immersive experience were quantified using the NASA Task Load Index (NASA-TLX) and a customized motion-sickness questionnaire. No statistically significant inter-group difference in grand-averaged SSVEP SNR was detected between MR and VR (F (1,20) = 1.002, p = 0.329). Numerically higher ACC and ITR values were observed for MR (19.51 vs. 18.57 bits/min), but these performance gaps did not reach statistical significance. In exploratory paired comparisons, MR showed lower directional imbalance scores and higher game-experience scores (Z = − 3.431, p = 0.001, r = − 0.75). Across both environments, low-frequency flicker (10, 11.25 Hz) elicited significantly stronger SSVEP responses than high-frequency stimuli (15, 18 Hz, p < 0.001). MR generates consistent SSVEP neural signals alongside favorable subjective perceptual experience, with only non-significant numerical gains in offline decoding efficiency. Key limitations of this work include a small young-adult sample, exclusive offline signal analysis without closed-loop online validation, and inherent optical confounds from differing MR/VR rendering luminance and contrast. These findings provide preliminary offline evidence supporting MR’s usability for home-based rehabilitation and smart-environment BCI applications, while larger-scale online experiments are required to confirm its objective decoding benefits.