论文标题
EEG-NEXT：一种现代化的转向，用于分类脑电图的认知活动
EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG
论文作者
论文摘要
基于脑电图（EEG）的脑部计算机界面（BCI）系统的主要挑战之一是学习主题/会话不变特征，以在端到端的判别设置中对认知活动进行分类。我们提出了一个新颖的端到端机器学习管道EEG-NEXT，该管道促进了转移学习：通过自适应登录，取代最新的（SOTA）图像分类模型）作为骨干网络。在公开可用的数据集（Physionet Sleep Cassette和BNCI2014001）上，我们通过跨主体验证对SOTA进行基准测试，并在认知活动分类中表现出提高的准确性，以及跨同类群的更好的概括性。
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.
