Source: The Register: Biting the hand that feeds
A UC Davis team demonstrated that existing brain-computer interface hardware, paired with refined machine learning translation models, can convert neural signals into usable communication fast enough for real employment—not just laboratory tasks. This moves BCIs from symbolic proof-of-concept (spelling words) into functional workplace integration, where latency and accuracy directly affect economic participation. The practical constraint was always the software layer, not the electrodes, which means BCIs could scale to working populations faster than hardware development cycles typically allow.