  Identifying and reconstructing what we see from brain activity gives us a
special insight into investigating how the biological visual system represents
the world. While recent efforts have achieved high-performance image
classification and high-quality image reconstruction from brain signals
collected by Functional Magnetic Resonance Imaging (fMRI) or
magnetoencephalogram (MEG), the expensiveness and bulkiness of these devices
make relevant applications difficult to generalize to practical applications.
On the other hand, Electroencephalography (EEG), despite its advantages of ease
of use, cost-efficiency, high temporal resolution, and non-invasive nature, has
not been fully explored in relevant studies due to the lack of comprehensive
datasets. To address this gap, we introduce EEG-ImageNet, a novel EEG dataset
comprising recordings from 16 subjects exposed to 4000 images selected from the
ImageNet dataset. EEG-ImageNet consists of 5 times EEG-image pairs larger than
existing similar EEG benchmarks. EEG-ImageNet is collected with image stimuli
of multi-granularity labels, i.e., 40 images with coarse-grained labels and 40
with fine-grained labels. Based on it, we establish benchmarks for object
classification and image reconstruction. Experiments with several commonly used
models show that the best models can achieve object classification with
accuracy around 60% and image reconstruction with two-way identification around
64%. These results demonstrate the dataset's potential to advance EEG-based
visual brain-computer interfaces, understand the visual perception of
biological systems, and provide potential applications in improving machine
visual models.
