Humans and machines learn together to win a competition

People using brain-computer interface are more efficient when both human and machine are allowed to learn. EPFL researchers trained two tetraplegic users to compete in the international Cybathlon BCI race. Both incrementally learned how to control the BCI, and obtained the best performances at the competition, confirming researchers' hypothesis that mutual learning plays a fundamental role in BCI training. Brain-computer interfaces (BCIs) are seen as a potential means by which severely physically impaired individuals can regain control of their environment. BCIs use the electrical activity in the brain to control an external device. They have seen growing use in people with severe motor disabilities, for communication (by controlling a keyboard), mobility (by controlling a powered wheelchair), and daily activities (by controlling a mechanical arm or other robotic devices). But establishing such an interface is not trivial. In a study published in the open-access journal PLOS Biology , a group of researchers at the École Polytechnique Fédérale de Lausanne in Geneva (Campus Biotech), led by José del R. Millán - Defitech Foundation Chair in Brain-Machine Interface , School of Engineering, suggests that letting humans adapt to machines improves their performance on a brain-computer interface. The scientists trained two tetraplegic subjects to compete in the Cybathlon BCI race 2016, an international competition where competitors control an on-screen avatar with brain-computer interfaces. The results suggest that the most dramatic improvements in computer-augmented performance are likely to occur when both human and machine are allowed to learn. How does it work?