The paper "Calibration-Free Error-Related Potential Decoding with Adaptive Subject-Independent Models: A Comparative Study" is available under the "Early Access" area on IEEE Xplore.
The paper is published in the journal IEEE Transactions on Medical Robotics and Bionics and deals with Error-related potentials (ErrPs) which provide an elegant method to improve human-machine interaction by detecting incorrect system behavior from the electroencephalogram of a human operator in real-time. This paper focuses on adaptive subject-independent decoding models particularly suitable for ErrP classification. As individualized decoding models require a time-consuming calibration phase, such models provide a promising alternative. Based on an investigation of the characteristics of inter-subject variations in the signal and feature space, the paper evaluates the performance of a decoding model solely trained on prior data and the effectiveness of adapting this model to a new subject in a comparative study.
F. M. Schönleitner, L. Otter, S. K. Ehrlich and G. Cheng, "Calibration-Free Error-Related Potential Decoding with Adaptive Subject-Independent Models: A Comparative Study," in IEEE Transactions on Medical Robotics and Bionics, doi: 10.1109/TMRB.2020.3012436.