用脑机接口+脉冲学习，实现个性化沉浸通信，省电6.46倍
Spiking Personalized Federated Learning for Brain-Computer Interface-Enabled Immersive Communication
- 脑机接口采集脑信号，结合脉冲神经网络做联邦学习
- 在真实数据上识别准确率最优，推理能耗降低6.46倍
- 适合脑信号异构性强、需隐私保护的可穿戴设备场景
本文提出一种新型沉浸式通信框架，利用脑机接口（BCI）获取脑信号以推断用户中心状态（如意图与感知不适），从而在个体差异显著的情况下实现更个性化、鲁棒的沉浸式自适应。我们开发了一种个性化联邦学习（PFL）模型来分析和处理收集到的脑信号，不仅兼容神经多样性脑信号数据，还防止敏感脑信号信息泄露。为解决能量受限的沉浸终端（如头戴显示设备）持续本地学习与推理中的能耗瓶颈，进一步将脉冲神经网络（SNN）嵌入PFL。通过稀疏的事件驱动脉冲计算，基于SNN的PFL在保持良好个性化性能的同时，显著降低了训练与推理的计算与能耗。在真实脑信号数据集上的实验表明，该方法在整体识别准确率上表现最佳，同时推理能耗相比传统人工神经网络基线降低6.46倍。
原文摘要 · Abstract (English)
This work proposes a novel immersive communication framework that leverages brain-computer interface (BCI) to acquire brain signals for inferring user-centric states (e.g., intention and perception-related discomfort), thereby enabling more personalized and robust immersive adaptation under strong individual variability. Specifically, we develop a personalized federated learning (PFL) model to analyze and process the collected brain signals, which not only accommodates neurodiverse brain-signal data but also prevents the leakage of sensitive brain-signal information. To address the energy bottleneck of continual on-device learning and inference on energy-limited immersive terminals (e.g., head-mounted display), we further embed spiking neural networks (SNNs) into the PFL. By exploiting sparse, event-driven spike computation, the SNN-enabled PFL reduces the computation and energy cost of training and inference while maintaining competitive personalization performance. Experiments on real brain-signal dataset demonstrate that our method achieves the best overall identification accuracy while reducing inference energy by 6.46$\times$ compared with conventional artificial neural network-based personalized baselines.
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