  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.
