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.

翻译：传统的侵入式脑机接口（iBCI）通常依赖于在实验室环境中的工作站上进行的神经解码过程，这阻碍了其日常应用。在边缘设备（如可穿戴设备）上实现这些解码过程，带来了与计算需求、处理速度和保持准确性相关的巨大挑战。本研究旨在寻找一种兼具强大性能和快速推理能力、适合边缘部署的优化神经解码骨干网络。我们执行了一系列涉及执行随机伸手任务的非人灵长类动物的神经解码实验，评估了四个候选模型——门控循环单元（GRU）、Transformer、接收加权键值（RWKV）和选择性状态空间模型（Mamba）——在多个指标上的表现：单会话解码、多会话解码、新会话微调、推理速度、校准速度和可扩展性。研究结果表明，尽管GRU模型提供了足够的准确性，但RWKV和Mamba模型因其卓越的推理和校准速度而更受青睐。此外，RWKV和Mamba符合缩放定律，在数据集更大和模型规模增加时表现出性能提升，而GRU的可扩展性较弱，Transformer模型所需的计算资源则呈难以承受的指数级增长。本文对这四种模型在各种场景下进行了全面的比较分析。这些结果对于确定一个能够处理日益增长的数据量且适合边缘部署的优化骨干网络至关重要。该分析为该领域的持续研究和实际应用提供了重要的见解。