Improved motor imagery BCI performance via task-unaware compression in the BELT Bayesian Edge-Cloud architecture

Brain-computer interface (BCI) systems have advanced with deep learning, but they are still limited by designs tied to specific applications, poor scalability, weak portability, the need for user-specific adaptation, and privacy concerns. We present BELT, a modular Bayesian Edge-Cloud architecture based on three principles: (i) Bayesian priors and posteriors to balance generalization and subject-specific learning, (ii) lightweight classifiers suitable for embedded devices, and (iii) task-aware compression to reduce bandwidth and improve privacy in edge-cloud communication. To show feasibility, we implement BELT-lite as an instantiation of BELT, a lightweight version built only from linear time-invariant operations, making it directly compatible with digital signal processing hardware. Using the BCI Competition IV-2a and IV-2b motor imagery datasets (18 subjects total, ten-fold cross-validation), BELT-lite achieved strong posterior performance after subject-specific fine-tuning: mean accuracy of 87.9%±6.8% on Dataset B and 80.6%±8.6% on Dataset A with data augmentation. After adaptation, four subjects from Dataset B and two from Dataset A exceeded 90% accuracy. On ARM Cortex-A7 hardware, BELT-lite achieved a mean latency of 6.75 ms per sample, significantly faster than EEGNet's 8.36 ms (p < 10-17)-a 21% speed improvement-at the cost of a modest but statistically significant accuracy reduction of approximately 2.7 percentage points compared to EEGNet. Network Tuning Blocks allowed partial parameter freezing: classifier-only fine-tuning incurred a modest 2-5% accuracy drop while substantially reducing training cost. Compression via the task-unaware autoencoder reduced data size by 3.3× while maintaining high accuracy: prior-model performance stayed within ≈1% of the uncompressed baseline (with slight improvements in some configurations), full posterior fine-tuning showed a ≈1% drop, and classifier-only fine-tuning incurred a ≈3% drop-an acceptable trade-off for privacy-preserving edge-cloud communication, where only a compressed latent representation is transmitted instead of raw EEG. Notably, this task-unaware autoencoder (trained solely to reconstruct the input) consistently outperformed autoencoders that also incorporated classification objectives (task-aware or task-only), providing the best accuracy-compression trade-off across all fine-tuning scenarios. These findings show that BELT provides a principled design for modular and scalable BCIs, while BELT-lite demonstrates that the approach supports accurate, efficient, and portable implementations. Together, they point toward BCI systems that are more practical, mass-producible, and privacy-aware, enabling wider use in real-world settings.