Swiss researchers say they improved the method for training brain-computer interfaces.

Brain-computer interfaces (BCIs) are emerging as a way for severely physically-impaired people to regain control of their environment, but setting them up is an immensely difficult task.

Researchers at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland say that letting humans adapt to machines improves their performance on a brain-computer interface (BCI).

Their research suggests that the most dramatic improvements in computer-augmented performance could happen when both human and machine are allowed to learn.

Typically, BCIs detect the brain’s electrical activity on 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 Swiss researchers hypothesised that performance could be improved if the operator and the machine both engaged in learning their mutual task.

To test this hypothesis, the authors enlisted two subjects, both tetraplegic adult men, for training 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 indicated they adapted normal brain wave patterns related to imagined movements, called sensorimotor rhythms, to control the avatar, and that these patterns became stronger over time, indicating that the subjects were learning how to better control the BCI during the training.

While some degree of learning likely takes place with even the simplest BCIs, the authors believe they have maximised 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 for a competition may also contribute to faster learning, the authors propose.

Their study is accessible here.