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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.
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.
With BCIs, the electrical activity is typically detected at one or more points of the surface of the skull, using non-invasive electroencephalographic electrodes, and fed to a computer program that, over time, could improve 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. It was suggested that the performance could be improved if the operator and the machine both engaged in learning their mutual task. However, direct evidences of such a learning mechanism were rare and fragmented.
At EPFL, two tetraplegic adult men were trained with a BCI system designed to detect multiple brain wave patterns. Training took place over several months, culminating in an international competition, called the Cybathlon, in which they competed against ten other teams. Each participant controlled an on-screen avatar in a multi-part race, requiring mastery of separate commands for spinning, jumping, sliding, and walking without stumbling. The two subjects marked the best three times overall in the competition, one of them winning the gold medal and the other holding the tournament record.
Electroencephalography recording of the subjects during their training showed that the brain wave patterns related to imagined movements (called sensorimotor rhythms), which have been adopted to control the avatar, became stronger over time, indicating that the subjects were learning how to better control the BCI during the training.
The authors believe they have maximized the chances for human learning by infrequent recalibration of the computer, leaving time for the human to better learn how to control the sensorimotor rhythms that would most efficiently evoke the desired avatar movement. Training in preparation of a competition may also contribute to faster learning, the authors propose.