Battelle announced on Wednesday that it will combine years of brain-computer interface (BCI) projects with its expertise in machine learning and artificial intelligence under a new award from the Defense Advanced Research Project Agency’s (DARPA) Artificial Intelligence Exploration program called Intelligent Neural Interfaces.
David Friedenberg, Battelle’s senior data scientist, and his team will build new decoding algorithms to make neural interfaces more robust over long periods of time using terabytes of neural data gathered during the NeuroLife sessions over the last five years.
“We want these systems to require less training and be more robust to interruptions and interference,” said Friedenberg. “We want to develop optimal decoders that automatically and continuously adapt to each individual and the BCI they’re using.”
The Battelle team has been working on an 18-month project, $1 million to solve the longstanding issues with decoder optimization and maintenance, improve accuracy and robustness. Battelle will develop a hybrid approach for stable long-term neural decoding using end-to-end deep neural network decoders to learn optimal features from the raw electrode recordings.
In addition, the team will develop models that account for changing context and continuously adapt accordingly using third-wave AI approaches. This allows for adjusting both the features and the decoder on-the-fly during normal system use to counteract nonstationarities and instabilities in the signal without requiring the user to intervene and recalibrate the system.
The next evolution of BCIs will occur as the systems move from the lab into the lives of users.
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