Multimodal Learning in Brain-Computer Interfaces: A Research Review on Applications

DOI

10.2991/978-94-6463-986-5_56How to use a DOI?

Keywords

Multimodal Learning; Brain-Computer Interfaces (BCIs); Federated Learning

Abstract

Brain-Computer Interfaces (BCIs) enable human-machine interaction by decoding neural activity, demonstrating broad application potential in fields such as medical rehabilitation and neural engineering. However, traditional unimodal BCI systems face limitations due to signal noise, individual variability, and task complexity, hindering their practical utility. Multimodal learning significantly enhances decoding accuracy, robustness, and generalization capabilities through the fusion of signals from multiple data sources, including electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and eye-tracking. This paper analyzes research progress in this field, covering signal acquisition and preprocessing, fusion strategies at the data layer, feature layer, and decision layer, typical applications (such as emotion recognition, vigilance monitoring, and neural decoding), and cutting-edge directions (including zero-shot learning, federated learning, and generative augmentation). Research indicates that multimodal fusion can improve BCI system performance by 15%–25% and enhance stability in complex environments by over 30%. This review systematically summarizes the theoretical framework and technical pathways of multimodal BCI, offering key insights to facilitate its transition from laboratory research to clinical applications.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rui Li
PY  - 2026
DA  - 2026/02/18
TI  - Multimodal Learning in Brain-Computer Interfaces: A Research Review on Applications
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 546
EP  - 554
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_56
DO  - 10.2991/978-94-6463-986-5_56
ID  - Li2026
ER  -