解决不同脑电头戴设备间信号差异问题，提升跨设备脑机接口性能。
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
- 通过空间蒸馏融合多电极信息，增强模型对设备差异的适应性。
- 在6个数据集上优于10种主流迁移学习方法，显著提升跨设备分类准确率。
- 首次将知识蒸馏用于跨头戴设备脑电信号迁移，适合脑机接口研发者。
非侵入式脑机接口（BCI）通过脑电图（EEG）信号实现用户与外部设备的直接交互，但不同头戴设备间电极数量和位置的差异导致信号解码困难。为此，本文提出基于空间蒸馏的分布对齐方法（SDDA），用于异构跨头戴设备迁移。SDDA首先通过空间蒸馏充分利用全部电极信息，再在输入、特征和输出空间进行分布对齐，以缓解源域与目标域间的显著差异。据我们所知，这是首个将知识蒸馏应用于跨头戴设备迁移的工作。在两个BCI范式下的六个EEG数据集上，SDDA在离线无监督域自适应和在线有监督域自适应场景中均表现优异，持续超越10种经典及先进迁移学习算法。
原文摘要 · Abstract (English)
A non-invasive brain-computer interface (BCI) enables direct interaction between the user and external devices, typically via electroencephalogram (EEG) signals. However, decoding EEG signals across different headsets remains a significant challenge due to differences in the number and locations of the electrodes. To address this challenge, we propose a spatial distillation based distribution alignment (SDDA) approach for heterogeneous cross-headset transfer in non-invasive BCIs. SDDA uses first spatial distillation to make use of the full set of electrodes, and then input/feature/output space distribution alignments to cope with the significant differences between the source and target domains. To our knowledge, this is the first work to use knowledge distillation in cross-headset transfers. Extensive experiments on six EEG datasets from two BCI paradigms demonstrated that SDDA achieved superior performance in both offline unsupervised domain adaptation and online supervised domain adaptation scenarios, consistently outperforming 10 classical and state-of-the-art transfer learning algorithms.
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