This paper tackles the problem of subject adaptive EEG-based visual
recognition. Its goal is to accurately predict the categories of visual stimuli
based on EEG signals with only a handful of samples for the target subject
during training. The key challenge is how to appropriately transfer the
knowledge obtained from abundant data of source subjects to the subject of
interest. To this end, we introduce a novel method that allows for learning
subject-independent representation by increasing the similarity of features
sharing the same class but coming from different subjects. With the dedicated
sampling principle, our model effectively captures the common knowledge shared
across different subjects, thereby achieving promising performance for the
target subject even under harsh problem settings with limited data.
Specifically, on the EEG-ImageNet40 benchmark, our model records the top-1 /
top-3 test accuracy of 72.6% / 91.6% when using only five EEG samples per class
for the target subject. Our code is available at
this https URL.

文章使用一种新方法，通过提高来自不同参与者但属于相同类的特征的相似性，从而实现学习主体无关的表示，进而在训练中仅使用少数样本即可准确预测基于 EEG 信号的视觉刺激类别。在有限数据情况下，即使采用了严格的问题设置，该方法在 EEG-ImageNet40 基准上实现了令人满意的表现，即面向目标主体每类只使用 5 个 EEG 样本时：72.6％ / 91.6％的 top-1 /top-3 测试精度。

基于 Inter-subject Contrastive Learning 的面向主体自适应的基于 EEG 的视觉认知

Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition

Building subject-independent deep learning models for EEG decoding faces the
challenge of strong covariate-shift across different datasets, subjects and
recording sessions. Our approach to address this difficulty is to explicitly
align feature distributions at various layers of the deep learning model, using
both simple statistical techniques as well as trainable methods with more
representational capacity. This follows in a similar vein as covariance-based
alignment methods, often used in a Riemannian manifold context. The methodology
proposed herein won first place in the 2021 Benchmarks in EEG Transfer Learning
(BEETL) competition, hosted at the NeurIPS conference. The first task of the
competition consisted of sleep stage classification, which required the
transfer of models trained on younger subjects to perform inference on multiple
subjects of older age groups without personalized calibration data, requiring
subject-independent models. The second task required to transfer models trained
on the subjects of one or more source motor imagery datasets to perform
inference on two target datasets, providing a small set of personalized
calibration data for multiple test subjects.

采用统计技术和可训练方法明确地对齐深度学习模型各层的特征分布，以应对跨数据集、受试者和记录会话的强协变量转移，从而构建针对 EEG 解码的主体无关的深度学习模型，该方法在 2021 年的 BEETL 比赛中获得了第一名。