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.

研究了 EEG 分类模型与通用时间序列分类模型之间的差异，并通过将时间序列模型与主题嵌入结合，实现了在所有受试者上训练一个联合主题条件分类器，其性能在三个数据集中与专用 EEG 模型相竞争。