Filtering information for search engines, acting as an opponent during a board game or recognizing images: Artificial intelligence has far outpaced human intelligence in certain tasks.
Several groups from the Freiburg excellence cluster BrainLinks-BrainTools led by neuroscientist private lecturer Dr. Tonio Ball are showing how ideas from computer science could revolutionize brain research.
In the scientific journal Human Brain Mapping they illustrate how a self-learning algorithm decodes human brain signals that were measured by an electroencephalogram (EEG).
Even though the algorithm was not given any characteristics ahead of time, it works as quickly and precisely as traditional systems that have been created to solve certain tasks based on predetermined brain signal characteristics, which are therefore not appropriate for every situation.
"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 computer scientist Robin Tibor Schirrmeister.
The researcher is using it to rewrite methods that the team has used for decoding EEG data: So-called artificial neural networks are the heart of the current project at BrainLinks-BrainTools.