Science Daily  April 1, 2024 Subject training requires collecting user-specific calibration data due to high inter-subject neural variability that limits the usability of generic decoders. Calibration is cumbersome and may produce inadequate data for building decoders, especially with naïve subjects. Researchers at UT Austin showed that a decoder trained on the data of a single expert is readily transferrable to inexperienced users via domain adaptation techniques allowing calibration-free Brain-computer interface (BCI) training. They introduced two real-time frameworks, (i) Generic Recentering (GR) through unsupervised adaptation and (ii) Personally Assisted Recentering (PAR) and evaluated it on naïve subjects to show that their […]