Brain-Computer Interface Signal Decoding Technology Based on Deep Learning

Ruotian Luo

2025

Abstract

With a special emphasis on the potential of sophisticated classification algorithms to improve overall system performance, this review article offers a thorough examination of the most recent developments in brain-computer interface (BCI) systems. The paper examines various methodologies, including adaptive learning, deep learning, and hybrid models, and evaluate their impact on decoding complex brain signals. Key findings highlight the superior efficacy of deep learning approaches such as LSTM-FCN and 1D CNN in improving accuracy and robustness. Transfer learning combined with advanced CSP algorithms also shows significant improvements in handling limited training data. Furthermore, the integration of deep learning with the EEG2Code method achieves remarkable information transfer rates. These advancements demonstrate transformative potential for BCI applications in healthcare, assistive technologies, and human-computer interaction. However, challenges remain in aligning algorithmic complexity with brain signal characteristics and ensuring practical deployment for end-users. Future research should focus on optimizing algorithms for real-time functionality, personalizing BCI systems, and exploring novel decoding modalities to further advance this transformative field.

Paper Citation

in Harvard Style

Luo R. (2025). Brain-Computer Interface Signal Decoding Technology Based on Deep Learning. In Proceedings of the 1st International Conference on Biomedical Engineering and Food Science - Volume 1: BEFS; ISBN 978-989-758-789-4, SciTePress, pages 27-32. DOI: 10.5220/0014300300004933

in Bibtex Style

@conference{befs25,
author={Ruotian Luo},
title={Brain-Computer Interface Signal Decoding Technology Based on Deep Learning},
booktitle={Proceedings of the 1st International Conference on Biomedical Engineering and Food Science - Volume 1: BEFS},
year={2025},
pages={27-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0014300300004933},
isbn={978-989-758-789-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Biomedical Engineering and Food Science - Volume 1: BEFS
TI - Brain-Computer Interface Signal Decoding Technology Based on Deep Learning
SN - 978-989-758-789-4
AU - Luo R.
PY - 2025
SP - 27
EP - 32
DO - 10.5220/0014300300004933
PB - SciTePress