Electroencephalogram Helps Few-Shot Learning

Abstract

Learning to categorize images with limited samples is a challenge for machines. However, humans can easily generalize from just a few examples. In this study, we propose that the remarkable ability of the human brain to generalize can be reflected in electroencephalogram (EEG) signals. These EEG-related features have the potential to enhance few-shot image classification. Our novel two-stage approach involves the following: first, we learn transferable knowledge from large labeled auxiliary sets by multimodal learning of images and EEG signals using contrastive learning. Then, we finetune the image encoder with novel classes that have only a few samples. We integrate this approach with the Triplet and ProxyNCA framework. Experimental results demonstrate an average improvement of 6.1% and 8.5% in terms of top-1 recall compared to the original Triplet and ProxyNCA methods, respectively. This work showcases the feasibility of leveraging brain signals to enhance few-shot learning.