  Neural representations induced by naturalistic stimuli offer insights into
how humans respond to stimuli in daily life. Understanding neural mechanisms
underlying naturalistic stimuli processing hinges on the precise identification
and extraction of the shared neural patterns that are consistently present
across individuals. Targeting the Electroencephalogram (EEG) technique, known
for its rich spatial and temporal information, this study presents a framework
for Contrastive Learning of Shared SpatioTemporal EEG Representations across
individuals (CL-SSTER). CL-SSTER utilizes contrastive learning to maximize the
similarity of EEG representations across individuals for identical stimuli,
contrasting with those for varied stimuli. The network employed spatial and
temporal convolutions to simultaneously learn the spatial and temporal patterns
inherent in EEG. The versatility of CL-SSTER was demonstrated on three EEG
datasets, including a synthetic dataset, a natural speech comprehension EEG
dataset, and an emotional video watching EEG dataset. CL-SSTER attained the
highest inter-subject correlation (ISC) values compared to the state-of-the-art
ISC methods. The latent representations generated by CL-SSTER exhibited
reliable spatiotemporal EEG patterns, which can be explained by properties of
the naturalistic stimuli. CL-SSTER serves as an interpretable and scalable
framework for the identification of inter-subject shared neural representations
in naturalistic neuroscience.
