Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

Neuroimaging and AI: Deciphering the Brain's Perception for Multimodal Content Synthesis

Overview of Multimodal Content Synthesis

The latest developments in neuroscientific research and AI have presented unprecedented opportunities for exploring the relationship between brain activity and the perception of diverse stimuli, such as images, videos, and audio. Multimodal synthesis technology is an ever-evolving field that aims to decode the complex mapping between the brain's activity and various forms of external stimuli. This exploration into brain-conditional multimodal synthesis offers potential breakthroughs in developing tangible brain-computer interface (BCI) systems and delving into the underlying cognitive mechanisms.

Neuroimaging Data and Brain Regions

Neuroimaging technologies such as fMRI, EEG, and MEG provide a window into the brain's intricate neural activities by capturing data on blood flow, electrical, and magnetic fields. Each technology offers distinct trade-offs between spatial and temporal resolution. Understanding these datasets is crucial for deciphering the functionalities and interactions of different brain regions, which in turn illuminates the complex processes of perception.

Moreover, identifying key brain regions involved in the processing of auditory, visual, and language information enables researchers to pinpoint the neural basis of perception. Regions such as the visual cortex, auditory cortex, and language-related areas in the frontal lobe play prominent roles in these perceptive tasks.

Generative Models in AI

Generative models have seen strides in progress, covering deterministic autoencoders (AEs), probabilistic models like variational autoencoders (VAEs), autoregressive models (AMs), and Generative Adversarial Networks (GANs). Their applications stretch across the realms of image, audio, and text synthesis. Conditional generative models introduce a new dimension of complexity by infusing conditional information into the generative process.

Latent Diffusion Models (LDMs) are particularly notable for their ability to generate high-quality images by integrating conditions into the denoising process. ControlNet and Versatile Diffusion stand out for their multimodal generation capabilities, leveraging guidance from paired text and images.

Methodology Taxonomy

The methodologies in brain-conditional multimodal content synthesis can be categorized into six distinct types based on their implementation architecture:

1. Mapping Brain to Prior Information: Mapping brain signals to semantic or detail priors within the pre-trained generative models.
2. Brain-Pretrain and Mapping: Involves a two-step process of pre-training on brain signals and then mapping to priors.
3. Brain-Pretrain, Finetune, and Align: Another two-step approach, emphasizing the alignment of priors with pre-trained models and fine-tuning.
4. Map, Train, and Finetune: Creates a connection between brain signals, priors, and stimuli, followed by training or fine-tuning the generative architecture.
5. End-to-End and Autoencoder-Based Aligning: Directly map brain signals to stimuli, either through a traditional training process or with deterministic autoencoder alignment.

The trade-offs between these methods vary in terms of training complexity, flexibility, data requirements, and interpretability.

Tasks and Implementation Strategies

Different tasks in AIGC-Brain research leverage various methods and technologies. For example, Image-Brain-Image (IBI) tasks make extensive use of I2I-LDMs that integrate detail priors and semantic conditions for image synthesis. In the Video-Brain-Video (VBV) domain, augmented diffusion models lead to improved video reconstruction from brain activity. Similarly, Sound-Brain-Sound (SBS) tasks see models like BSR employing autoregressive transformers to generate sound from brain signals.

Text-based tasks, such as Image&Video&Speech-Brain-Text (IBT, VBT, SBT), utilize autoregressive models to decode brain signals into linguistic descriptions. Multi-modal tasks are advancing towards more consolidated models capable of understanding and generating content across different modalities.

Quality Assessment and Insights

Quality assessments are indispensable to evaluate the synthesis results both qualitatively and quantitatively. While qualitative assessments show what is achievable in terms of reconstructing perception from brain signals, quantitative metrics offer a more objective measure of the models' performance. Metrics are tailored for different levels of features, from low-level details like pixel correlation to high-level semantic fidelity like CLIP embeddings. These assessments drive the progression of technology by highlighting areas for improvement and guiding new model development.

Future Directions

The field is approaching several significant challenges:

• Data Variability: The acquisition of higher quality, large-scale neuroimaging datasets is essential.
• Fidelity: Improving semantic and detail accuracy in content synthesis is crucial.
• Flexibility: Enhancing model adaptability to various datasets and tasks will promote generalization.
• Interpretability: Understanding neural processing during decoding enriches our comprehension of cognition.
• Real-time: Advancements in real-time decoding are vital for BCI systems.
• Multimodality: Developing unified models for brain-to-any multimodal generation is an upcoming frontier.

These technological landscapes and future aspirations chart a course towards deepening our understanding of brain function and the potential of AI-assisted brain signal decoding.