P3-FSLNet: A Compact Spatio-Temporal Model With Contrastive Few-Shot Learning for Subject-Independent P300 Detection in Devanagari Script-Based P300 Speller

Brain–computer interface (BCI) systems frequently necessitate time-intensive subject-specific calibration, thereby motivating the development of subject-independent P300 detection approaches. Existing methodologies that employ transfer learning and knowledge distillation encounter challenges with limited generalizability due to substantial intersubject variability and constrained data availability. Furthermore, their applicability is often questioned, as most lack comprehensive external validation and cross-script evaluation. To address these limitations, we introduce P3-FSLNet, a few-shot metalearning framework that integrates prototypical networks and contrastive learning within a spatial-temporal convolutional neural network augmented by dual-channel attention for the selection of relevant electroencephalogram channels. Episodic metatraining facilitates the transfer of robust knowledge across different subjects. Evaluated on a self-recorded Devanagari script dataset, P3-FSLNet attains a classification accuracy of 93.17%, surpassing state-of-the-art methods by 1%–14%, while simultaneously reducing trainable parameters by up to 400 times. External validation using English-script datasets from BCI Competition II and III confirms its robustness in cross-subject and cross-script generalization. These findings demonstrate the efficacy of P3-FSLNet and represent a substantial advancement toward the development of script-agnostic P300 spellers that are lightweight, scalable, and conducive to multilingual applications.