EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG

TL;DR

EEG-NeXt introduces a transfer learning pipeline using a modern ConvNeXt architecture to improve cross-subject EEG-based cognitive activity classification, demonstrating superior accuracy and generalizability on public datasets.

Contribution

The paper presents a novel end-to-end EEG classification method that combines trial alignment, frequency localization, and pretrained ConvNeXt for enhanced transfer learning.

Findings

Improved classification accuracy over state-of-the-art methods.

Enhanced generalizability across different cohorts.

Effective transfer learning for EEG-based cognitive activity recognition.

Abstract

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.