Abstract
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.
Abstract (translated)
基于脑电图（EEG）的脑机接口（BCI）受到会随时间、个体变化的非平稳神经信号的影响，这对无主体特定模型的应用推广造成了限制，并促进了资源受限平台上自适应和个性化学习的发展。可编程忆阻器硬件为这种部署后的适应提供了有前景的基础；然而，在实际应用中面临着权重分辨率有限、器件变异性大、编程动力学非线性和设备耐久度有限等挑战。在这项工作中，我们展示了在铁电忆阻突触装置上可以部署脉冲神经网络（SNN），用于具有现实设备约束条件下的自适应EEG运动想象解码。我们制造并表征了铁电突触，并对其进行了建模。我们在两种互补的部署策略下评估了一种卷积-递归SNN架构：（i）使用铁电突触模型进行感知训练；以及（ii）将软件训练权重转移到设备上，然后通过低开销重新调优完成适应。为了实现高效的自适应调整，我们引入了一种感知权重更新策略，在此策略中，基于梯度的更新数字累加并在阈值被超过时才转换为离散编程事件，从而模拟非线性和状态依赖性编程动力学，并减少了编程频率。这两种部署方式均实现了与当前最佳软件SNN相媲美的分类性能。此外，通过仅重新训练最终网络层实现特定主体的学习转移能够提高分类准确性。这些结果表明，可编程铁电硬件能够在脉冲神经网络中支持稳健且低开销的适应性调整，为神经信号的人工智能处理开辟了一条实用途径。
URL
https://arxiv.org/abs/2601.00020
