Title: EEG-ImageNet: A Benchmark for Pre-training and Cross-Time Generalization of EEG-based Visual Decoding
Primary area: datasets and benchmarks
Keywords: EEG, visual stimuli, computer vision, multi-modality

Abstract: Exploring brain activity in relation to visual perception provides insights into the biological representation of the world. 
While functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) have enabled effective image classification and reconstruction, their high cost and bulk limit practical use. 
Electroencephalography (EEG), by contrast, offers low cost and excellent temporal resolution, but its potential has been limited by the scarcity of large, high-quality datasets and by block-design experiments that introduce temporal confounds.
To fill this gap, we present EEG-ImageNet, a benchmark for pre-training and cross-time generalization of visual decoding from EEG. 
We collected EEG data from 16 participants while they viewed 4,000 images sampled from ImageNet, with image stimuli annotated at multiple levels of granularity. 
Our design includes two stages separated in time to allow cross-time generalization and avoid block-design artifacts.
We also introduce benchmarks tailored to non-block design classification, as well as pre-training experiments to assess cross-time and cross-subject generalization. 
These findings highlight the dataset's potential to enhance EEG-based visual brain-computer interfaces, deepen our understanding of visual perception in biological systems, and suggest promising applications for improving machine vision models.