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A researcher applies contact gel, to achieve better brain signal transmission quality.

Researchers at the University of Freiburg's excellence cluster BrainLinks-BrainTools have demonstrated how a self-learning algorithm decodes human brain waves.

Credit: Michael Veit

Researchers at the University of Freiburg's excellence cluster BrainLinks-BrainTools in Germany are showing how ideas from computer science could revolutionize brain research.

The researchers demonstrated how a self-learning algorithm decodes human signals measured by an electroencephalogram (EEG).

"Our software is based on brain-inspired models that have proven to be most helpful to decode various natural signals such as phonetic sounds," says Freiburg's Robin Tibor Schirrmeister.

The team employed the software to rewrite artificial neural networks used for decoding EEG data as part of the BrainLinks-BrainTools program.

Schirrmeister notes the system learns to recognize and differentiate between certain behavioral patterns from various movements as it works.

In addition, the researchers developed the software to create cards from which they can understand the decoding decisions.

"Unlike the old method, we are now able to go directly to the raw signals that the EEG records from the brain," says Freiburg's Tonio Ball.

From University of Freiburg
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