用生成模型统一表示脑影像数据，降低采集成本并提升脑机接口公平性。
Empowering Functional Neuroimaging: A Pre-trained Generative Framework for Unified Representation of Neural Signals
- 通过生成式AI将多模态脑影像映射到统一表征空间。
- 生成的数据与真实脑活动模式一致，提升下游任务性能。
- 可为少数群体补足数据，改善脑机接口模型公平性。
多模态功能脑成像能系统分析大脑机制，并为脑机接口（BCI）解码提供判别性表征。然而，其获取受限于高昂成本和可行性问题，且特定群体数据不足影响了BCI模型的公平性。为此，我们提出一种基于生成式人工智能的统一表征框架，用于多模态功能脑影像。该框架通过将多模态脑影像映射至统一表征空间，能够生成采集受限模态及少数群体的数据。实验表明，该框架生成的数据与真实脑活动模式一致，可揭示大脑机制，并提升下游任务表现。更重要的是，通过为少数群体扩充数据，显著增强了模型公平性。整体上，该框架为降低多模态脑影像采集成本、提升BCI模型公平性提供了新范式。
原文摘要 · Abstract (English)
Multimodal functional neuroimaging enables systematic analysis of brain mechanisms and provides discriminative representations for brain-computer interface (BCI) decoding. However, its acquisition is constrained by high costs and feasibility limitations. Moreover, underrepresentation of specific groups undermines fairness of BCI decoding model. To address these challenges, we propose a unified representation framework for multimodal functional neuroimaging via generative artificial intelligence (AI). By mapping multimodal functional neuroimaging into a unified representation space, the proposed framework is capable of generating data for acquisition-constrained modalities and underrepresented groups. Experiments show that the framework can generate data consistent with real brain activity patterns, provide insights into brain mechanisms, and improve performance on downstream tasks. More importantly, it can enhance model fairness by augmenting data for underrepresented groups. Overall, the framework offers a new paradigm for decreasing the cost of acquiring multimodal functional neuroimages and enhancing the fairness of BCI decoding models.
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