轻量级脑机接口模型，支持低功耗嵌入式部署
SwitchBraidNet: Quantisation-Aware Lightweight Architecture for Hybrid Brain-Computer Interface
- 双路径时序编织提取多尺度脑电特征
- INT8精度下仅3.03KB，混合任务速率64.82比特/分钟
- 适合资源受限的便携式脑机接口系统
融合运动想象（MI）与稳态视觉诱发电位（SSVEP）的混合脑机接口可实现高维神经解码，但通常超出嵌入式硬件的计算能力。为此，我们提出SwitchBraidNet，一种专为低功耗部署设计的紧凑型脑电分类架构。该模型采用双路径时序编织结构提取多尺度振荡特征，引入自适应挤压-激励空间开关实现电极选择，并使用对数方差读出层直接编码频带功率。通过在OpenBMI数据集上进行系统性量化感知训练，我们在FP32、FP16和INT8精度下对比了四类基线模型。实验结果表明，该模型在保持高性能的同时显著提升效率：在FP16下达到MI准确率69.49%、SSVEP准确率93.48%，混合信息传输速率达64.82比特/分钟；其INT8模型仅占3.03KB存储空间，在不同精度下均维持高准确率，充分验证其在低功耗嵌入式脑机接口部署中的适用性。
原文摘要 · Abstract (English)
Hybrid brain-computer interfaces (BCIs) that integrate motor imagery (MI) and steady-state visual evoked potentials (SSVEP) provide high-dimensional neural decoding but typically exceed the computational limits of embedded hardware. To address this, we propose SwitchBraidNet, a compact EEG classification architecture designed for low-power deployment. The model employs a dual-path temporal braid to extract multiscale oscillatory features, an adaptive squeeze-and-excitation spatial switch for electrode gating, and a log-variance readout layer for direct band-power encoding. Furthermore, through systematic quantisation-aware training on the OpenBMI dataset, we compared SwitchBraidNet against four established baselines across FP32, FP16, and INT8 precisions. Experimental results demonstrate superior efficiency and performance, achieving MI accuracy of 69.49% (FP16), SSVEP accuracy of 93.48% (FP32), and a hybrid information transfer rate of 64.82 bits/min (FP16). With an INT8 footprint of only 3.03 KB, SwitchBraidNet maintains high accuracy across varying numerical precisions, demonstrating its suitability for low-power embedded BCI deployment.
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