Traditional invasive Brain-Computer Interfaces (iBCIs) typically depend on
neural decoding processes conducted on workstations within laboratory settings,
which prevents their everyday usage. Implementing these decoding processes on
edge devices, such as the wearables, introduces considerable challenges related
to computational demands, processing speed, and maintaining accuracy. This
study seeks to identify an optimal neural decoding backbone that boasts robust
performance and swift inference capabilities suitable for edge deployment. We
executed a series of neural decoding experiments involving nonhuman primates
engaged in random reaching tasks, evaluating four prospective models, Gated
Recurrent Unit (GRU), Transformer, Receptance Weighted Key Value (RWKV), and
Selective State Space model (Mamba), across several metrics: single-session
decoding, multi-session decoding, new session fine-tuning, inference speed,
calibration speed, and scalability. The findings indicate that although the GRU
model delivers sufficient accuracy, the RWKV and Mamba models are preferable
due to their superior inference and calibration speeds. Additionally, RWKV and
Mamba comply with the scaling law, demonstrating improved performance with
larger data sets and increased model sizes, whereas GRU shows less pronounced
scalability, and the Transformer model requires computational resources that
scale prohibitively. This paper presents a thorough comparative analysis of the
four models in various scenarios. The results are pivotal in pinpointing an
optimal backbone that can handle increasing data volumes and is viable for edge
implementation. This analysis provides essential insights for ongoing research
and practical applications in the field.

传统侵入性脑机接口通常依赖于在实验室环境中工作站上进行的神经解码过程，制约了其日常使用。本研究旨在识别一种在边缘设备上实施的具有强大性能和快速推理能力的最佳神经解码框架。通过进行一系列神经解码实验，评估四个潜在的模型（Gated Recurrent Unit（GRU）, Transformer, Receptance Weighted Key Value（RWKV）和 Selective State Space model (Mamba)）在多种指标上的表现，如单次会话解码、多次会话解码、新会话微调、推理速度、校准速度和可扩展性。研究结果表明，虽然 GRU 模型提供了足够的准确性，但 RWKV 和 Mamba 模型由于具有更优异的推理速度和校准速度而更可取。此外，RWKV 和 Mamba 符合比例定律，在较大的数据集和增加的模型大小下表现出更好的性能，而 GRU 的可扩展性不太明显，Transformer 模型要求的计算资源过大。本文在各种场景下对这四个模型进行了全面的比较分析。研究结果对于确定能够处理不断增长的数据量并可用于边缘实施的最佳框架至关重要，为该领域的持续研究和实际应用提供了重要见解。