Mind Video

Mind Video - Reconstructing Videos from Brain Activity

Key Features:

• Utilizes continuous functional magnetic resonance imaging (fMRI) data
• Reconstructs high-quality videos from brain activity
• Extension of the fMRI-Image reconstruction work called Mind-Vis
• Addresses the challenge of recovering continuous visual experiences
• Employs a two-module pipeline for bridging the gap between image and video brain decoding
• Learn general visual fMRI features through large-scale unsupervised learning
• Distills semantic-related features using multimodal contrastive learning
• Uses co-training with an augmented stable diffusion model for fine-tuning feature learning
• Demonstrates high semantic accuracy, including motions and scene dynamics
• Progressive learning scheme for assimilating nuanced semantic information

Use Cases:

• Neuroscientific research on visual perception and cognition
• Monitoring brain activity during visual experiences
• Understanding the hierarchical nature of visual cortex processing
• Advancements in brain-computer interfaces
• Enhancing virtual reality and augmented reality technologies
• Investigations into brain disorders related to visual processing

If you are looking for an advanced AI tool that can reconstruct high-quality videos from brain activity, Mind Video is the perfect solution. By utilizing continuous functional magnetic resonance imaging (fMRI) data, this tool goes beyond traditional imaging techniques to provide a unique glimpse into the visual experiences of the brain.

Mind Video builds upon the success of the previous fMRI-Image reconstruction work called Mind-Vis. Its two-module pipeline bridges the gap between image and video brain decoding, ensuring accurate and detailed reconstructions. The tool's first module focuses on learning general visual fMRI features through large-scale unsupervised learning, while the second module distills semantic-related features using multimodal contrastive learning.

The standout feature of Mind Video lies in its adaptive pipeline, which consists of an fMRI encoder and an augmented stable diffusion model trained separately and then fine-tuned together. The progressive learning scheme employed by the tool allows the encoder to learn brain features through multiple stages, resulting in high semantic accuracy and outperforming previous state-of-the-art approaches.

An attention analysis of the transformers decoding fMRI data reveals the dominance of the visual cortex in processing visual spatiotemporal information and the hierarchical nature of the encoder's layers in extracting structural and abstract visual features.

With applications ranging from neuroscientific research to brain-computer interfaces and virtual reality technologies, Mind Video opens up new possibilities for understanding the complexities of the human brain. Experience the power of AI-driven video reconstruction with Mind Video today.

Give Mind Video a try and unlock the hidden visual experiences of the mind!