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

TL;DR

This paper introduces a novel inter-subject contrastive learning method for EEG-based visual recognition, enabling accurate predictions with limited target data by learning subject-independent features shared across individuals.

Contribution

It proposes a new contrastive learning approach that enhances subject-independent EEG feature representations, improving recognition accuracy with minimal target data.

Findings

Achieved 72.6% top-1 accuracy with only five samples per class.

Effectively captures shared knowledge across subjects.

Demonstrates robustness in limited-data scenarios.

Abstract

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.