A Stanford University research team presents a brain-computer interface for translating speech-related neural activity into text (speech BCI) in the new paper A High-performance Speech Neuroprosthesis. Theirs is the first speech BCI to record impulse activity from intracortical microelectrode arrays and could benefit people unable to produce clear utterances due to diseases such as stroke and ALS.
In the new paper Decoding Speech From Non-Invasive Brain Recordings, a research team from Meta AI and the Inria Saclay Centre presents a single end-to-end architecture for decoding natural speech processing from non-invasive magnetoencephalography (MEG) or electroencephalography (EEG) brain recordings that can detect macroscopic brain signals in real-time.
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.