Calibration-Free Error-Related Potential Decoding With Adaptive Subject-Independent Models: A Comparative Study

Error-related potentials (ErrPs) provide an elegant method to improve human-machine interaction by detecting incorrect system behavior from the electroencephalogram of a human operator in real time. In this paper, we focus 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, we evaluate 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. Our results show that such a generalized model can decode ErrPs with an acceptable average accuracy of 72.7 ± 9.66% and that supervised adaptation can significantly improve the accuracy of the generalized model after adaptation with 85 trials by on average +3.8 ± 5.1%. We show that adaptation of subject-independent decoding models is superior to the traditional calibration procedure. Unsupervised adaptation only proved effective for some subjects and requires further attention to be practical for a broader range of subjects. Consequently, our work contributes to the development of calibration-free ErrP decoding in the broader scope of enhancing usability of ErrPs for human-machine interaction.