Tetraplegics benefit from mutual learning via brain-computer interface

Brain-computer interfaces (BCIs) are seen as a potential means by which severely physically impaired individuals can regain control of their environment, but establishing such an interface is not trivial. A study publishing May 10 in the open access journal PLOS Biology, by a group of researchers at the École Polytechnique Fédérale de Lausanne in Geneva, Switzerland, suggests that letting humans adapt to machines improves their performance on a brain-computer interface. The study of tetraplegic subjects training to compete in the Cybathlon avatar race suggests that the most dramatic improvements in computer-augmented performance are likely to occur when both human and machine are allowed to learn.

BCIs, which use the electrical activity in the brain to control an object, have seen growing use in people with high spinal cord injuries, for communication (by controlling a keyboard), mobility (by controlling a powered wheelchair), and daily activities (by controlling a mechanical arm or other robotic device).

Typically, the electrical activity is detected at one or more points of the surface of the skull, using non-invasive electroencephalographic electrodes, and fed through a computer program that, over time, improves its responsiveness and accuracy through learning.

As machine learning algorithms have become both faster and more powerful, researchers have largely focused on increasing decoding performance by identifying optimal pattern recognition algorithms. The authors hypothesized that performance could be improved if the operator and the machine both engaged in learning their mutual task.

Read more: https://www.sciencedaily.com/releases/2018/05/180510145828.htm

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