Learning from Heterogeneous EEG Signals with Differentiable Channel Reordering
This addresses a domain-specific problem for EEG researchers by enabling better transfer of trained systems across datasets with different collection protocols, though it is incremental as it builds on existing attention mechanisms.
The paper tackles the problem of training neural networks on EEG signals with inconsistent channel ordering and number across datasets, proposing CHARM, a differentiable method that uses attention to reorder channels to a canonical order, and demonstrates improved classification and transfer learning on four EEG datasets.
We propose CHARM, a method for training a single neural network across inconsistent input channels. Our work is motivated by Electroencephalography (EEG), where data collection protocols from different headsets result in varying channel ordering and number, which limits the feasibility of transferring trained systems across datasets. Our approach builds upon attention mechanisms to estimate a latent reordering matrix from each input signal and map input channels to a canonical order. CHARM is differentiable and can be composed further with architectures expecting a consistent channel ordering to build end-to-end trainable classifiers. We perform experiments on four EEG classification datasets and demonstrate the efficacy of CHARM via simulated shuffling and masking of input channels. Moreover, our method improves the transfer of pre-trained representations between datasets collected with different protocols.