A Review of Brain-Computer Interface Technologies: Signal Acquisition Methods and Interaction Paradigms
Yifan Wang, Cheng Jiang, Chenzhong Li
TL;DR
This review analyzes the interplay between brain-computer interface paradigms and signal acquisition technologies, detailing classic MI, P300, and SSVEP paradigms alongside hybrid approaches. It categorizes signal acquisition into non-implantation, intervention, and implantation, assessing methods from EEG and MEG to invasive ECoG and fully implanted cortical arrays. The paper argues that paradigm design and signal acquisition are mutually reinforcing, showing how advances in sensors, materials, and invasive techniques spur new paradigms and vice versa. Looking ahead, it highlights naturalistic and hybrid paradigms, advanced materials for higher-fidelity sensing, and AI integration as key levers for more efficient, user-friendly, and versatile BCIs with substantial clinical and everyday impact.
Abstract
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.