Introduction to brain–computer interface: research trends and applications

: Qaisar, Saeed Mian; Subasi Abdulhamit

: Subasi, Abdulhamit; Qaisar, Saeed Mian; Bhoi, Akash Kumar; Srinivasu, Parvathaneni Naga

: 1st Edition

Publisher: Elsevier

: 2025

: Artificial Intelligence Applications for Brain–Computer Interfaces

: Artificial Intelligence Applications in Healthcare and Medicine

: 1

: 20

: 978-0-443-33414-6

: 978-0-443-33415-3

DOI: https://doi.org/10.1016/B978-0-443-33414-6.00001-0

: https://doi.org/10.1016/b978-0-443-33414-6.00001-0

A broader review of the artificial intelligence (AI) and sensing methods used in brain–computer interfaces (BCIs) is given in this chapter. The brain and external equipment, such as computers and electrical gadgets, can communicate via the BCIs. Using a variety of noninvasive wearable sensors, it is now feasible to analyze neural signals from the brain to gain insights from brain patterns. The neural signals are preprocessed for noise removal and conditioning for the feature extraction and classification stages. The signal processing and analysis techniques are used for preconditioning and feature extraction. The classification is often carried out using the robust AI algorithms. The approaches covered have a variety of uses, from individual experiences to commercial applications. Particularly the applications of BCIs in assistive, mental state estimation, gaming, and entertainment are explored, showing how the signal processing and AI improve performance in various domains. The chapter also examines the latest AI-specific methods for BCIs, including deep learning and ensemble learning. This investigation lays the groundwork for future study and advancement in this exciting area by illuminating AI’s critical role in improving the BCIs.