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.

翻译：非侵入式脑机接口（BCI）通常通过脑电图（EEG）信号实现用户与外部设备的直接交互。然而，由于电极数量与位置存在差异，跨不同头戴设备的脑电信号解码仍面临重大挑战。为解决此问题，本文提出一种基于空间蒸馏的分布对齐（SDDA）方法，用于非侵入式脑机接口中的异构跨头戴设备迁移。SDDA首先通过空间蒸馏充分利用完整电极信息，随后采用输入/特征/输出空间分布对齐以应对源域与目标域间的显著差异。据我们所知，这是首个在跨头戴设备迁移中应用知识蒸馏的研究。基于两种脑机接口范式的六个脑电数据集的广泛实验表明，SDDA在离线无监督域适应与在线有监督域适应场景中均取得优越性能，持续超越10种经典及前沿迁移学习算法。