Since their inception more than 50 years ago, Brain-Computer Interfaces (BCIs) have held
promise to compensate for functions lost by people with disabilities through allowing direct …

In the era of Artificial Intelligence Generated Content (AIGC), conditional multimodal
synthesis technologies (eg, text-to-image) are dynamically resha** the natural content …

Reconstructing visual stimulus representation is a significant task in neural decoding. Until
now, most studies have considered functional magnetic resonance imaging (fMRI) as the …

In recent years, the field of electroencephalography (EEG) analysis has witnessed
remarkable advancements, driven by the integration of machine learning and artificial …

In this article, we explore the potential of using latent diffusion models, a family of powerful
generative models, for the task of reconstructing naturalistic music from …

We present Alljoined1, a dataset built specifically for EEG-to-Image decoding. Recognizing
that an extensive and unbiased sampling of neural responses to visual stimuli is crucial for …

Decoding human visual representations from brain activity data is a challenging but
arguably essential task with an understanding of the real world and the human visual …

In an era where neuroscience dances with computational advances, the power to “visualize”
one's thoughts at image-level is no longer confined to the realm of science fiction. This …

Our visual experience in daily life are dominated by dynamic change. Decoding such
dynamic information from brain activity can enhance the understanding of the brain's visual …

In the field of cross-subject emotion recognition using electroencephalogram (EEG) signals,
significant challenges arise due to substantial inter-individual differences and the …