  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
https://github.com/DeepBCI/Deep-BCI/tree/master/1_Intelligent_BCI/Inter_Subject_Contrastive_Learning_for_EEG.
