Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
This work addresses a domain-specific problem for brain-computer interface users by enabling more robust EEG classification across heterogeneous headsets, though it is incremental as it builds on existing transfer learning methods.
The paper tackled the challenge of decoding EEG signals across different headsets by proposing a spatial distillation based distribution alignment (SDDA) approach, which achieved superior performance in offline and online domain adaptation scenarios, consistently outperforming 10 state-of-the-art transfer learning algorithms.
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.