A Review of Brain-Computer Interface Technologies: Signal Acquisition Methods and Interaction Paradigms
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Abstract (arXiv)
Brain-Computer Interface (BCI) technology facilitates direct communication between the human brain and external devices, representing a substantial advancement in human-machine interaction. This review provides an in-depth analysis of various BCI paradigms, including classic paradigms, current classifications, and hybrid paradigms, each with distinct characteristics and applications. Additionally, we explore a range of signal acquisition methods, classified into non-implantation, intervention, and implantation techniques, elaborating on their principles and recent advancements. By examining the interdependence between paradigms and signal acquisition technologies, this review offers a comprehensive perspective on how innovations in one domain propel progress in the other. The goal is to present insights into the future development of more efficient, user-friendly, and versatile BCI systems, emphasizing the synergy between paradigm design and signal acquisition techniques and their potential to transform the field.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 85 references that are in the map. First posted on arXiv 1 Mar 2025.
Builds on
3 of 85 references.
References · 3
- 2016EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfacescited by 628
- 2023Estimating and approaching maximum information rate of noninvasive visual brain-computer interfacecited by 4
- 2013Neural Dust: An Ultrasonic, Low Power Solution for Chronic Brain-Machine Interfacescited by 3
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Citing papers per year: 2025: 1.
1 paper.