Detecting Glaucoma With a Portable Brain-Computer Interface for Objective Assessment of Visual Function Loss.

Importance: 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 Participants: This 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 Measures: Receiver 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 Relevance: The 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.

Polychromatic SSVEP stimuli with subtle flickering adapted to brain-display interactions.

OBJECTIVE: Interactive displays armed with natural user interfaces (NUIs) will likely lead the next breakthrough in consumer electronics, and brain-computer interfaces (BCIs) are often regarded as the ultimate NUI-enabling machines to respond to human emotions and mental states. Steady-state visual evoked potentials (SSVEPs) are a commonly used BCI modality due to the ease of detection and high information transfer rates. However, the presence of flickering stimuli may cause user discomfort and can even induce migraines and seizures. With the aim of designing visual stimuli that can be embedded into video images, this study developed a novel approach to induce detectable SSVEPs using a composition of red/green/blue flickering lights. APPROACH: Based on the opponent theory of colour vision, this study used 32 Hz/40 Hz rectangular red-green or red-blue LED light pulses with a 50% duty cycle, balanced/equal luminance and 0°/180° phase shifts as the stimulating light sources and tested their efficacy in producing SSVEP responses with high signal-to-noise ratios (SNRs) while reducing the perceived flickering sensation. MAIN RESULTS: The empirical results from ten healthy subjects showed that dual-colour lights flickering at 32 Hz/40 Hz with a 50% duty cycle and 180° phase shift achieved a greater than 90% detection accuracy with little or no flickering sensation. SIGNIFICANCE: As a first step in developing an embedded SSVEP stimulus in commercial displays, this study provides a foundation for developing a combination of three primary colour flickering backlights with adjustable luminance proportions to create a subtle flickering polychromatic light that can elicit SSVEPs at the basic flickering frequency.

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology.

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.

Habituation of steady-state visual evoked potentials in response to high-frequency polychromatic foveal visual stimulation.

In an attempt to develop safe and robust methods for monitoring migraineurs' brain states, we explores the feasibility of using white, red, green and blue LED lights flickering around their critical flicker fusion (CFF) frequencies as foveal visual stimuli for inducing steady-state visual evoked potentials (SSVEP) and causing discernible habituation trends. After comparing the habituation indices, the multi-scale entropies and the time dependent intrinsic correlations of their SSVEP signals, we reached a tentative conclusion that sharp red and white light pulses flickering barely above their CFF frequencies can replace commonly used 13Hz stimuli to effectively cause SSVEP habituation among normal subjects. Empirical results showed that consecutive short bursts of light can produce more consistent responses than a single prolonged stimulation. Since these high frequency stimuli do not run the risk of triggering migraine or seizure attacks, further tests of these stimuli on migraine patients are warranted in order to verify their effectiveness.

SNR analysis of high-frequency steady-state visual evoked potentials from the foveal and extrafoveal regions of human retina.

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.

An fMRI study of abrupt-awake episodes during behavioral microsleeps.

This paper reports the brain activation patterns of five subjects who were abruptly awakened from microsleeps in a simulated automotive driving experiment. By comparing the BOLD signals between behavioral microsleep (BM), abrupt awakening (AA) and post-abrupt awakening (post-AA) stages, we observed that visual area, frontal cortex, limbic lobe manifested more intense activation during the AA stage while frontal cortex, temporal cortex, primary motor area and insula were more activated during the post-AA stage. These results suggested that the subjects were likely in mental states differ from those associated with decision making processes as they went through and emerged from the abrupt awakening episodes.

Consistent sparse representations of EEG ERP and ICA components based on wavelet and chirplet dictionaries.

A randomized search algorithm for sparse representations of EEG event-related potentials (ERPs) and their statistically independent components is presented. This algorithm combines greedy matching pursuit (MP) technique with covariance matrix adaptation evolution strategy (CMA-ES) to select small number of signal atoms from over-complete wavelet and chirplet dictionaries that offer best approximations of quasi-sparse ERP signals. During the search process, adaptive pruning of signal parameters was used to eliminate redundant or degenerative atoms. As a result, the CMA-ES/MP algorithm is capable of producing accurate efficient and consistent sparse representations of ERP signals and their ICA components. This paper explains the working principles of the algorithm and presents the preliminary results of its use.