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arXiv · 2212.04951

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

Andac Demir·Iya Khalil·Bulent Kiziltan
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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.

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BibTeXRIS

Andac Demir, Iya Khalil, Bulent Kiziltan. 2022-12-08. EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG. https://arxiv.org/abs/2212.04951

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