Decoding Natural Images from EEG for Object Recognition
This work addresses the problem of low signal-to-noise ratio in EEG-based image decoding for researchers in neural decoding and brain-computer interfaces, though it appears incremental as it builds on existing contrastive learning methods.
The paper tackled decoding natural images from EEG signals for object recognition using a self-supervised contrastive learning framework, achieving a top-1 accuracy of 15.6% and top-5 accuracy of 42.8% in 200-way zero-shot tasks.
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.