  Pathology diagnosis based on EEG signals and decoding brain activity holds
immense importance in understanding neurological disorders. With the
advancement of artificial intelligence methods and machine learning techniques,
the potential for accurate data-driven diagnoses and effective treatments has
grown significantly. However, applying machine learning algorithms to
real-world datasets presents diverse challenges at multiple levels. The
scarcity of labelled data, especially in low regime scenarios with limited
availability of real patient cohorts due to high costs of recruitment,
underscores the vital deployment of scaling and transfer learning techniques.
In this study, we explore a real-world pathology classification task to
highlight the effectiveness of data and model scaling and cross-dataset
knowledge transfer. As such, we observe varying performance improvements
through data scaling, indicating the need for careful evaluation and labelling.
Additionally, we identify the challenges of possible negative transfer and
emphasize the significance of some key components to overcome distribution
shifts and potential spurious correlations and achieve positive transfer. We
see improvement in the performance of the target model on the target (NMT)
datasets by using the knowledge from the source dataset (TUAB) when a low
amount of labelled data was available. Our findings indicate a small and
generic model (e.g. ShallowNet) performs well on a single dataset, however, a
larger model (e.g. TCN) performs better on transfer and learning from a larger
and diverse dataset.
