# Multimodal Brain-Computer Interfaces: AI-powered Decoding Methodologies

arXiv: https://arxiv.org/abs/2502.02830 · HTML: https://mlsanity.com/paper/2502.02830/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies](https://mlsanity.com/paper/2510.22095/) (2025)
- [Uncertainty-Aware Cross-Modal Knowledge Distillation with Prototype Learning for Multimodal Brain-Computer Interfaces](https://mlsanity.com/paper/2507.13092/) (2025)
- [Uncertainty-Resilient Multimodal Learning via Consistency-Guided Cross-Modal Transfer](https://mlsanity.com/paper/2511.15741/) (2025)

## Abstract (arXiv)

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.