Electroencephalography (EEG)-based brain-computer interfaces (BCIs) are strongly affected by non-stationary neural signals that vary across sessions and individuals, limiting the generalization of subject-agnostic models and motivating adaptive and personalized learning on resource-constrained platforms. Programmable memristive hardware offers a promising substrate for such post-deployment adaptation; however, practical realization is challenged by limited weight resolution, device variability, nonlinear programming dynamics, and finite device endurance. In this work, we show that spiking neural networks (SNNs) can be deployed on ferroelectric memristive synaptic devices for adaptive EEG-based motor imagery decoding under realistic device constraints. We fabricate, characterize, and model ferroelectric synapses. We evaluate a convolutional-recurrent SNN architecture under two complementary deployment strategies: (i) device-aware training using a ferroelectric synapse model, and (ii) transfer of software-trained weights followed by low-overhead on-device re-tuning. To enable efficient adaptation, we introduce a device-aware weight-update strategy in which gradient-based updates are accumulated digitally and converted into discrete programming events only when a threshold is exceeded, emulating nonlinear, state-dependent programming dynamics while reducing programming frequency. Both deployment strategies achieve classification performance comparable to state-of-the-art software-based SNNs. Furthermore, subject-specific transfer learning achieved by retraining only the final network layers improves classification accuracy. These results demonstrate that programmable ferroelectric hardware can support robust, low-overhead adaptation in spiking neural networks, opening a practical path toward personalized neuromorphic processing of neural signals.

翻译：基于脑电图（EEG）的脑机接口（BCI）受非平稳神经信号的强烈影响，这些信号在不同会话和个体间存在差异，限制了与受试者无关模型的泛化能力，从而推动了对资源受限平台上的自适应与个性化学习的需求。可编程忆阻硬件为此类部署后适应提供了有前景的载体；然而，其实际实现面临权重分辨率有限、器件变异性、非线性编程动态特性以及器件耐久度有限等挑战。在本工作中，我们展示了脉冲神经网络（SNN）可在实际器件约束下，部署于铁电忆阻突触器件上，用于基于EEG的运动想象解码的自适应处理。我们制备、表征并建模了铁电突触。我们在两种互补的部署策略下评估了一种卷积-循环SNN架构：（i）使用铁电突触模型进行器件感知训练，以及（ii）迁移软件训练权重后进行低开销的片上重调谐。为实现高效适应，我们引入了一种器件感知权重更新策略，其中基于梯度的更新在数字域累积，仅当超过阈值时才转换为离散的编程事件，从而在模拟非线性、状态相关的编程动态特性的同时降低编程频率。两种部署策略均实现了与最先进的基于软件的SNN相当的分类性能。此外，通过仅重新训练网络最后层实现的受试者特异性迁移学习提高了分类准确率。这些结果表明，可编程铁电硬件能够支持脉冲神经网络中稳健、低开销的适应，为神经信号的个性化神经形态处理开辟了一条实用路径。