  A major goal in neuroscience is to discover neural data representations that
generalize. This goal is challenged by variability along recording sessions
(e.g. environment), subjects (e.g. varying neural structures), and sensors
(e.g. sensor noise), among others. Recent work has begun to address
generalization across sessions and subjects, but few study robustness to sensor
failure which is highly prevalent in neuroscience experiments. In order to
address these generalizability dimensions we first collect our own
electroencephalography dataset with numerous sessions, subjects, and sensors,
then study two time series models: EEGNet (Lawhern et al., 2018) and TOTEM
(Talukder et al., 2024). EEGNet is a widely used convolutional neural network,
while TOTEM is a discrete time series tokenizer and transformer model. We find
that TOTEM outperforms or matches EEGNet across all generalizability cases.
Finally through analysis of TOTEM's latent codebook we observe that
tokenization enables generalization.
