Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific
conditions of pre-trained diffusion models. These approaches, while producing high-quality …

Decoding images from non-invasive electroencephalographic (EEG) signals has been a
grand challenge in understanding how the human brain process visual information in real …

Human's perception of the visual world is shaped by the stereo processing of 3D
information. Understanding how the brain perceives and processes 3D visual stimuli in the …

In this paper, we propose a novel framework for multimodal contrastive learning utilizing a
quantum encoder to integrate EEG (electroencephalogram) and image data. This …

Despite significant progress in visual decoding with fMRI data, its high cost and low temporal
resolution limit widespread applicability. To address these challenges, we introduce …

Brain decoders that reconstruct language from semantic representations have the potential
to improve communication for people with impaired language production. However, training …

Multi-modal Emotion Recognition (MER) has demonstrated competitive performance in
affective computing, owing to synthesizing information from diverse modalities. However …

Reconstructing visual stimuli from functional Magnetic Resonance Imaging (fMRI) enables
fine-grained retrieval of brain activity. However, the accurate reconstruction of diverse …

As AI systems become increasingly powerful, the need for safe AI has become more
pressing. Humans are an attractive model for AI safety: as the only known agents capable of …

Vision-brain understanding aims to extract semantic information about brain signals from
human perceptions. Existing deep learning methods for vision-brain understanding are …