Inter-subject Contrastive Learning for Subject Adaptive EEG-based Visual Recognition
This addresses the problem of EEG-based visual recognition for brain-computer interfaces by enabling adaptation to new subjects with minimal data, though it is incremental in improving existing methods.
The paper tackles subject adaptive EEG-based visual recognition by transferring knowledge from source subjects to a target subject with limited data, achieving 72.6% top-1 and 91.6% top-3 accuracy on the EEG-ImageNet40 benchmark using only five EEG samples per class.
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.