- We introduce a novel framework CHAnnel Reordering Module (CHARM) for training a single model across varying EEG collections that can differ both in number and location of electrodes.
- Our differentiable module uses an attention mechanism on multichannel EEG signals, identifies the location of each channel from their content, and remaps them to a canonical order.
- We perform experiments on four EEG classification datasets for the tasks of seizure, artifact detection, and abnormal EEG recordings.
- We demonstrate the efficacy of via simulated shuffling and masking of input channels.
- We also propose a channel masking and shuffling augmentation strategy for multi-channel input to improve robustness of standard models.
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