Transformer-based neural decoders with large parameter counts, pre-trained on large-scale datasets, have recently outperformed classical machine learning models and small neural networks on brain-computer interface (BCI) tasks. However, their large parameter counts and high computational demands hinder deployment in power-constrained implantable systems. To address this challenge, we introduce BrainDistill, a novel implantable motor decoding pipeline that integrates an implantable neural decoder (IND) with a task-specific knowledge distillation (TSKD) framework. Unlike standard feature distillation methods that attempt to preserve teacher representations in full, TSKD explicitly prioritizes features critical for decoding through supervised projection. Across multiple neural datasets, IND consistently outperforms prior neural decoders on motor decoding tasks, while its TSKD-distilled variant further surpasses alternative distillation methods in few-shot calibration settings. Finally, we present a quantization-aware training scheme that enables integer-only inference with activation clipping ranges learned during training. The quantized IND enables deployment under the strict power constraints of implantable BCIs with minimal performance loss.

翻译：基于Transformer的神经解码器凭借大规模参数及预训练数据，近期在脑机接口任务中超越了经典机器学习模型与小规模神经网络。然而，其庞大的参数量与高计算需求阻碍了其在功耗受限的可植入系统中的部署。为应对这一挑战，我们提出BrainDistill——一种集成可植入神经解码器与任务特定知识蒸馏框架的新型可植入运动解码流程。相较于试图完整保留教师表征的标准特征蒸馏方法，TSKD通过监督投影显式优先提取对解码至关重要的特征。在多个神经数据集上，IND在运动解码任务中持续优于现有神经解码器，而其经TSKD蒸馏的变体在少样本校准场景中进一步超越了其他蒸馏方法。最后，我们提出一种量化感知训练方案，该方案通过训练过程中学习激活截断范围，实现仅需整型运算的推理。量化后的IND可在可植入脑机接口的严格功耗限制下部署，且性能损失极小。