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.

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.

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.

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.

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.

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.