A Brain–Computer Interface Based on Miniature-Event-Related Potentials Induced by Very Small Lateral Visual Stimuli
Traditional visual BCIs preferred large-size stimuli to elicit large-amplitude EEG features. But long-term exposure to the irritating stimuli could easily cause visual fatigue to users. We develop a method to recognize weakest ever BCI signals induced by very small visual stimuli, and implement a vision friendly and high-efficiency BCI speller.
Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG
In recent years, there have been significant advances in wearable, mobile, dry-electrode electroencephalography (EEG) systems. These are yielding exciting new possibilities for scientific research, clinical diagnostics and therapeutics, and brain-computer interfaces (BCI) outside the clinic or laboratory. However, these systems have been limited to a handful of channels mostly for applications of low-dimensional signal analysis in gaming and command control. Here we describe and evaluate the first high-resolution dry mobile BCI system supporting real-time artifact rejection, imaging of distributed cortical network dynamics, and inference of cognitive state with a 64-channel dry-electrode wireless EEG headset.