  One of the main challenges in electroencephalogram (EEG) based brain-computer
interface (BCI) systems is learning the subject/session invariant features to
classify cognitive activities within an end-to-end discriminative setting. We
propose a novel end-to-end machine learning pipeline, EEG-NeXt, which
facilitates transfer learning by: i) aligning the EEG trials from different
subjects in the Euclidean-space, ii) tailoring the techniques of deep learning
for the scalograms of EEG signals to capture better frequency localization for
low-frequency, longer-duration events, and iii) utilizing pretrained ConvNeXt
(a modernized ResNet architecture which supersedes state-of-the-art (SOTA)
image classification models) as the backbone network via adaptive finetuning.
On publicly available datasets (Physionet Sleep Cassette and BNCI2014001) we
benchmark our method against SOTA via cross-subject validation and demonstrate
improved accuracy in cognitive activity classification along with better
generalizability across cohorts.
