The current assessment of visual field loss in diseases such as glaucoma is affected by the subjectivity of patient responses and the lack of portability of standard perimeters.OBJECTIVE To describe the development and initial validation of a portable brain-computer interface (BCI) for objectively assessing visual function loss. DESIGN, SETTING, AND PARTICIPANTSThis case-control study involved 62 eyes of 33 patients with glaucoma and 30 eyes of 17 healthy participants. Glaucoma was diagnosed based on a masked grading of optic disc stereophotographs. All participants underwent testing with a BCI device and standard automated perimetry (SAP) within 3 months. The BCI device integrates wearable, wireless, dry electroencephalogram and electrooculogram systems and a cellphone-based head-mounted display to enable the detection of multifocal steady state visual-evoked potentials associated with visual field stimulation. The performances of global and sectoral multifocal steady state visual-evoked potentials metrics to discriminate glaucomatous from healthy eyes were compared with global and sectoral SAP parameters. The repeatability of the BCI device measurements was assessed by collecting results of repeated testing in 20 eyes of 10 participants with glaucoma for 3 sessions of measurements separated by weekly intervals. MAIN OUTCOMES AND MEASURESReceiver operating characteristic curves summarizing diagnostic accuracy. Intraclass correlation coefficients and coefficients of variation for assessing repeatability. RESULTS Among the 33 participants with glaucoma, 19 (58%) were white, 12 (36%) were black, and 2 (6%) were Asian, while among the 17 participants with healthy eyes, 9 (53%) were white, 8 (47%) were black, and none were Asian. The receiver operating characteristic curve area for the global BCI multifocal steady state visual-evoked potentials parameter was 0.92 (95% CI, 0.86-0.96), which was larger than for SAP mean deviation (area under the curve, 0.81; 95% CI, 0.72-0.90), SAP mean sensitivity (area under the curve, 0.80; 95% CI, 0.69-0.88; P = .03), and SAP pattern standard deviation (area under the curve, 0.77; 95% CI, 0.66-0.87; P = .01). No statistically significant differences were seen for the sectoral measurements between the BCI and SAP. Intraclass coefficients for global and sectoral parameters ranged from 0.74 to 0.92, and mean coefficients of variation ranged from 3.03% to 7.45%. CONCLUSIONS AND RELEVANCEThe BCI device may be useful for assessing the electrical brain responses associated with visual field stimulation. The device discriminated eyes with glaucomatous neuropathy from healthy eyes in a clinically based setting. Further studies should investigate the feasibility of the BCI device for home-based testing as well as for detecting visual function loss over time.

With brain-computer interface (BCI) applications in mind, we analyzed the amplitudes and the signal-to-noise ratios (SNR) of steady-state visual evoked potentials (SSVEP) induced in the foveal and extra-foveal regions of human retina. Eight subjects (age 20-55) have been exposed to 2° circular and 16°-18° annular visual stimulation produced by white LED lights flickering between 5Hz and 65Hz in 5Hz increments. Their EEG signals were recorded using a 64-channel NeuroScan system and analyzed using non-parametric spectral and canonical convolution techniques. Subjects' perception of flickering and their levels of comfort towards the visual stimulation were also noted. Almost all subjects showed distinctively higher SNR in their foveal SSVEP responses between 25Hz and 45Hz. They also noticed less flickering and felt more comfortable with the visual stimulation between 30Hz and 45Hz. These empirical evidences suggest that lights flashing above the critical flicker fusion rates (CFF) of human vision may be used as effective and comfortable stimuli in SSVEP BCI applications.

Cybersickness is a critical problem of virtual reality (VR) applications. This study found an objective method to quantize the cybersickness level using the bio-signals, including electroencephalography (EEG) and electrocardiography (ECG). These results demonstrated a higher level of cybersickness caused power of gamma band (>32Hz) stronger and enhanced subjects' heart rate as well. The variations of these bio-signals could be an indicator to quantize people's cybersickness level and be applied for a warning system for VR devices.

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