用AI算法提升多模态脑机接口的解码能力，助力脑信号与设备直接通信
Multimodal Brain-Computer Interfaces: AI-powered Decoding Methodologies
- 融合跨模态映射与序列建模，提升脑数据解码精度
- 涵盖视觉、语言和情感解码的多领域应用现状
- 适合神经科学与AI交叉研究者参考
脑机接口（BCI）实现大脑与外部设备的直接通信。本文综述了支持多模态BCI的核心解码算法，剖析关键构成要素，整合多样化方法，并全面分析该领域的当前进展。重点阐述了跨模态映射与序列建模等算法创新如何提升脑数据解码效果，系统梳理了在视觉、言语及情感解码方面的最新应用研究。展望未来，文章关注多模态Transformer等新兴架构的影响，讨论脑数据异质性与常见误差等挑战。本综述旨在为具有神经科学背景与人工智能背景的研究者搭建桥梁，提供对AI赋能多模态脑机接口的全面理解。
原文摘要 · Abstract (English)
Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the field. We emphasize algorithmic advancements in cross-modality mapping, sequential modeling, besides classic multi-modality fusion, illustrating how these novel AI approaches enhance decoding of brain data. The current literature of BCI applications on visual, speech, and affective decoding are comprehensively explored. Looking forward, we draw attention on the impact of emerging architectures like multimodal Transformers, and discuss challenges such as brain data heterogeneity and common errors. This review also serves as a bridge in this interdisciplinary field for experts with neuroscience background and experts that study AI, aiming to provide a comprehensive understanding for AI-powered multimodal BCIs.
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