# A Review of Brain-Computer Interface Technologies: Signal Acquisition Methods and Interaction Paradigms

arXiv: https://arxiv.org/abs/2503.16471 · HTML: https://mlsanity.com/paper/2503.16471/

## Key facts

- Cited by 1 papers 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 (most cited first, 3 of 3)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Estimating and approaching maximum information rate of noninvasive visual brain-computer interface](https://mlsanity.com/paper/2308.13232/) (2023)
- [Neural Dust: An Ultrasonic, Low Power Solution for Chronic Brain-Machine Interfaces](https://mlsanity.com/paper/1307.2196/) (2013)

## Built on by (most cited first, 1 of 1)

- [Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies](https://mlsanity.com/paper/2510.22095/) (2025)

## 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.