Electroencephalogram (EEG)-based P300 speller brain-computer interfaces (BCIs) face three main challenges: low signal-to-noise ratio (SNR), poor generalization, and time-consuming calibration. We propose SpellerSSL, a framework that combines self-supervised learning (SSL) with P300 aggregation to address these issues. First, we introduce an aggregation strategy to enhance SNR. Second, to achieve generalization in training, we employ a customized 1D U-Net backbone and pre-train the model on both cross-domain and in-domain EEG data. The pretrained model is subsequently fine-tuned with a lightweight ERP-Head classifier for P300 detection, which adapts the learned representations to subject-specific data. Our evaluations on calibration time demonstrate that combining the aggregation strategy with SSL significantly reduces the calibration burden per subject and improves robustness across subjects. Experimental results show that SSL learns effective EEG representations in both in-domain and cross-domain, with in-domain achieving a state-of-the-art character recognition rate of 94% with only 7 repetitions and the highest information transfer rate (ITR) of 21.86 bits/min on the public II-B dataset. Moreover, in-domain SSL with P300 aggregation reduces the required calibration size by 60% while maintaining a comparable character recognition rate. To the best of our knowledge, this is the first study to apply SSL to P300 spellers, highlighting its potential to improve both efficiency and generalization in speller BCIs and paving the way toward an EEG foundation model for P300 speller BCIs. The code is available at: https://github.com/Emotiv/SpellerSSL-NeurIPS2025
Modeling long-context electroencephalography (EEG) signals is critical for learning generalizable EEG representations, as EEG recordings are acquired at high sampling rates and often span extended durations that capture evolving neural states beyond short task- specific windows. Transformer-based models have shown promise in modeling short sequences of a few seconds; however, their quadratic complexity limits scalability to longer contexts. Moreover, substantial variability in electrode montages across datasets and recording devices poses a major challenge for building EEG models that generalize across domains. We propose SAMBA, a self-supervised learning framework with a Mamba- based U-shaped encoder-decoder architecture, which effectively captures long-range temporal dependencies and montage variability in EEG data. Leveraging the inherent ability of Mamba in processing long context sizes, we introduce: (1) Temporal Semantic Random Masking for semantic-level sequence reconstruction, (2) a Multi-Head Differential Mamba module to suppress redundancy and emphasize salient temporal structures, and (3) a Coordinate-Driven Spatial Embedding that learns unified embeddings in a three- dimensional Euclidean space, enabling robustness across devices. Experiments on thirteen EEG datasets across diverse tasks, electrode configurations, and sequence durations demonstrate that SAMBA achieves competitive or improved performance compared to recent state-of-the-art methods, while maintaining low memory consumption and efficient inference. The learned spatial weight maps exhibit close alignment with task-relevant neurophysiological regions, highlighting the interpretability of SAMBA. The code is available at: https://github.com/Jiazhen-Hong/SAMBA