Application and Development of Embedded Brain-Computer Interface and Artificial Intelligence Deep Learning

DOI

10.2991/978-94-6463-864-6_86How to use a DOI?

Keywords

Brain-Computer Interface; Artificial Intelligence; Deep Learning

Abstract

With the development of artificial intelligence and wearable devices, brain-computer interface (BCI) has gradually shifted from basic research to practical applications. The data collected by traditional embedded brain-computer interfaces (BCIs) are often highly complex and uncertain, posing significant challenges for effective signal processing and interpretation. Moreover, the presence of large amounts of noise further obscures meaningful brainwave patterns, making it extremely difficult to isolate relevant signals. This is comparable to searching for a spotted mosquito deep within a dense forest—where the signal of interest is easily buried under layers of irrelevant or misleading data. In such a context, the ability to rapidly and accurately detect and extract key brainwave signals from massive, noisy datasets has become one of the most critical technical challenges in the current development of BCIs. Overcoming this issue is essential for advancing real-time neural decoding and improving the overall reliability and efficiency of brain-computer communication systems, laying the foundation for future applications in both clinical neuroscience and human-computer interaction.

Copyright

© 2025 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  - Siyuan Yu
PY  - 2025
DA  - 2025/10/23
TI  - Application and Development of Embedded Brain-Computer Interface and Artificial Intelligence Deep Learning
BT  - Proceedings of the 2025 2nd International Conference on Electrical Engineering and Intelligent Control (EEIC 2025)
PB  - Atlantis Press
SP  - 1002
EP  - 1014
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-864-6_86
DO  - 10.2991/978-94-6463-864-6_86
ID  - Yu2025
ER  -