  As with most other data domains, EEG data analysis relies on rich
domain-specific preprocessing. Beyond such preprocessing, machine learners
would hope to deal with such data as with any other time series data. For EEG
classification many models have been developed with layer types and
architectures we typically do not see in time series classification.
Furthermore, typically separate models for each individual subject are learned,
not one model for all of them. In this paper, we systematically study the
differences between EEG classification models and generic time series
classification models. We describe three different model setups to deal with
EEG data from different subjects, subject-specific models (most EEG
literature), subject-agnostic models and subject-conditional models. In
experiments on three datasets, we demonstrate that off-the-shelf time series
classification models trained per subject perform close to EEG classification
models, but that do not quite reach the performance of domain-specific
modeling. Additionally, we combine time-series models with subject embeddings
to train one joint subject-conditional classifier on all subjects. The
resulting models are competitive with dedicated EEG models in 2 out of 3
datasets, even outperforming all EEG methods on one of them.
