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

• URL: http://arxiv.org/abs/2212.04951v1
• Date: Thu, 8 Dec 2022 10:15:52 GMT
• Title: EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG
• Authors: Andac Demir, Iya Khalil, Bulent Kiziltan
• Abstract summary: 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. We propose a novel end-to-end machine learning pipeline, EEG-NeXt, which facilitates transfer learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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