Electroencephalography (EEG) signals, known for convenient non-invasive acquisition but low signal-to-noise ratio, have recently gained substantial attention due to the potential to decode natural images. This paper presents a self-supervised framework to demonstrate the feasibility of learning image representations from EEG signals, particularly for object recognition. The framework utilizes image and EEG encoders to extract features from paired image stimuli and EEG responses. Contrastive learning aligns these two modalities by constraining their similarity. With the framework, we attain significantly above-chance results on a comprehensive EEG-image dataset, achieving a top-1 accuracy of 15.6% and a top-5 accuracy of 42.8% in challenging 200-way zero-shot tasks. Moreover, we perform extensive experiments to explore the biological plausibility by resolving the temporal, spatial, spectral, and semantic aspects of EEG signals. Besides, we introduce attention modules to capture spatial correlations, providing implicit evidence of the brain activity perceived from EEG data. These findings yield valuable insights for neural decoding and brain-computer interfaces in real-world scenarios. The code will be released on https://github.com/eeyhsong/NICE-EEG.

翻译：脑电图（EEG）信号以其便捷的非侵入式采集和低信噪比特性著称，近年来因解码自然图像的潜力而受到广泛关注。本文提出一种自监督框架，论证了从EEG信号学习图像表征（尤其是用于目标识别）的可行性。该框架利用图像编码器和EEG编码器从配对图像刺激与EEG响应中提取特征，并通过对比学习约束两种模态的相似性以实现对齐。在综合EEG图像数据集上，该框架取得了显著高于随机水平的结果：在200类别零样本任务中，Top-1准确率达15.6%，Top-5准确率达42.8%。此外，我们通过解析EEG信号的时域、空域、频谱和语义特征开展了大量实验，以探究其生物学合理性。同时引入注意力模块捕捉空间相关性，为EEG数据中感知到的脑活动提供隐性证据。这些发现为真实场景中的神经解码与脑机接口提供了重要启示。相关代码将发布于https://github.com/eeyhsong/NICE-EEG。