用事件驱动设计高效脑机接口解码器，大幅降低功耗与资源占用。
Architectural Exploration of Hybrid Neural Decoders for Neuromorphic Implantable BMI
- 引入可调事件滤波器，直接在神经事件流上做筛选与检测
- 解码精度达R²=0.73，计算量和内存降低5-23倍
- 适合低功耗植入式或可穿戴脑机系统，无需传统信号预处理
本文提出一种面向类脑植入式脑机接口（Neu-iBMI）的高效解码流程，利用基于事件的神经传感方案产生的稀疏神经事件数据。我们设计了可调事件滤波器（EvFilter），兼具事件筛选与尖峰检测功能（EvFilter-SPD），使解码所需处理事件数分别减少192倍和554倍。所提流程在基于人工神经网络（ANN）和脉冲神经网络（SNN）的解码器上实现最高R²=0.73的解码性能，无需进行信号恢复、尖峰检测或分类等传统iBMI步骤。SNN解码器相较传统神经网络与LSTM解码器，计算量与内存需求降低5-23倍；而ST-NN解码器性能接近LSTM解码器，资源消耗仅为后者的2.5倍。该简化流程显著降低计算与内存开销，适用于低功耗、植入式或可穿戴式iBMI系统。
原文摘要 · Abstract (English)
This work presents an efficient decoding pipeline for neuromorphic implantable brain-machine interfaces (Neu-iBMI), leveraging sparse neural event data from an event-based neural sensing scheme. We introduce a tunable event filter (EvFilter), which also functions as a spike detector (EvFilter-SPD), significantly reducing the number of events processed for decoding by 192X and 554X, respectively. The proposed pipeline achieves high decoding performance, up to R^2=0.73, with ANN- and SNN-based decoders, eliminating the need for signal recovery, spike detection, or sorting, commonly performed in conventional iBMI systems. The SNN-Decoder reduces computations and memory required by 5-23X compared to NN-, and LSTM-Decoders, while the ST-NN-Decoder delivers similar performance to an LSTM-Decoder requiring 2.5X fewer resources. This streamlined approach significantly reduces computational and memory demands, making it ideal for low-power, on-implant, or wearable iBMIs.
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