Research Article
Artificial Intelligence in Brain-Computer Interfaces: Current Studies and Emerging Trends
@INPROCEEDINGS{10.4108/eai.22-5-2026.2365161, author={Yauyu Wang}, title={Artificial Intelligence in Brain-Computer Interfaces: Current Studies and Emerging Trends}, proceedings={Proceedings of the 4th International Conference on Image, Algorithms, and Artificial Intelligence, ICIAAI 2026, 22-24 May 2026, Singapore, Singapore}, publisher={EAI}, proceedings_a={ICIAAI}, year={2026}, month={8}, keywords={Brain Computer Interface Artificial Intelligence Machine Learning}, doi={10.4108/eai.22-5-2026.2365161} }- Yauyu Wang
Year: 2026
Artificial Intelligence in Brain-Computer Interfaces: Current Studies and Emerging Trends
ICIAAI
EAI
DOI: 10.4108/eai.22-5-2026.2365161
Abstract
Neurological disorders such as amyotrophic lateral sclerosis (ALS) or stroke impair the control of nerves over muscles, preventing people from living autonomously. Brain Computer Interface (BCI) offers a pragmatic answer, but suffers from scalability issues. This paper discussed the drawbacks of existing BCIs and how AI can be used in order to overcome them. By systematically reviewing and analyzing recent literature, this paper discusses how generative models and adaptive algorithms overcome the bottlenecks of traditional BCI. The combination of artificial intelligence (AI) and BCI solves the fundamental bottlenecks in traditional BCI, including low generalization capability, lack of sufficient data, and high cost. Generative AI-driven data augmentation improves the classification performance in BCIs up to 10-18%. The introduction of intelligent algorithms to adaptively optimize a BCI can significantly reduce its dial-in period, allowing it to operate without expert supervision while maintaining an adaptive response in real time.
