PhD electronic researcher Ildar Rakhmatulin and brain-computer interface developer Sebastian Völkl open-source an inexpensive, high-precision, easy-to-maintain PIEEG board that can convert a Raspberry Pi into a brain-computer interface for measuring and processing eight real-time EEG (Electroencephalography) signals.
University of Toronto researchers propose a BERT-inspired training approach as a self-supervised pretraining step to enable deep neural networks to leverage newly and publicly available massive EEG (electroencephalography) datasets for downstream brain-computer-interface (BCI) applications.
Personal computers and mobile devices are in their heyday. Researchers are swarming standalone AI, focusing on how to automate self-learning intelligent systems. The interfaces for wearables meanwhile are evolving from smart screens to gesture commands, like those often seen in AR and VR commercials.