Self-distillation with beta label smoothing-based cross-subject transfer learning for P300 classification

The P300 speller is one of the most well-known brain-computer interface (BCI) systems, offering users a novel way to communicate with their environment by decoding brain activity. However, most P300-based BCI systems require a longer calibration phase to develop a subject-specific model, which can be inconvenient and time-consuming. Additionally, it is challenging to implement cross-subject P300 classification due to significant inter-individual variations. To address these issues, this study proposes a calibration-free approach for P300 signal detection. Specifically, we incorporate self-distillation along with a beta label smoothing method to enhance model generalization and overall system performance, which can not only enable the distillation of informative knowledge from the electroencephalogram (EEG) data of other subjects but effectively reduce individual variability. The results conducted on the publicly available OpenBMI dataset demonstrate that the proposed method achieves statistically significantly higher performance compared to state-of-the-art approaches. Notably, the average character recognition accuracy of our method reaches up to 97.37% without the need for calibration. And information transfer rate and visualization further confirm its effectiveness. This method holds great promise for future developments in BCI applications. •This work constructs a calibration-free approach based on a deep convolutional neural network for P300 detection by utilizing the information from other subjects.•It incorporates self-distillation with a beta label smoothing method to improve the model generalization and system performances.•The proposed method yields statistically significantly higher performances when compared with several baseline methods.