TY - JOUR
T1 - Non-Invasive Brain-Computer Interfaces
T2 - State of the Art and Trends
AU - Edelman, Bradley J.
AU - Zhang, Shuailei
AU - Schalk, Gerwin
AU - Brunner, Peter
AU - Müller-Putz, Gernot
AU - Guan, Cuntai
AU - He, Bin
N1 - Publisher Copyright:
© 2024 The Authors.
PY - 2025
Y1 - 2025
N2 - Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to widely influence research, clinical and recreational use. Non-invasive BCI approaches are particularly common as they can impact a large number of participants safely and at a relatively low cost. Where traditional non-invasive BCIs were used for simple computer cursor tasks, it is now increasingly common for these systems to control robotic devices for complex tasks that may be useful in daily life. In this review, we provide an overview of the general BCI framework as well as the various methods that can be used to record neural activity, extract signals of interest, and decode brain states. In this context, we summarize the current state-of-the-art of non-invasive BCI research, focusing on trends in both the application of BCIs for controlling external devices and algorithm development to optimize their use. We also discuss various open-source BCI toolboxes and software, and describe their impact on the field at large.
AB - Brain-computer interface (BCI) is a rapidly evolving technology that has the potential to widely influence research, clinical and recreational use. Non-invasive BCI approaches are particularly common as they can impact a large number of participants safely and at a relatively low cost. Where traditional non-invasive BCIs were used for simple computer cursor tasks, it is now increasingly common for these systems to control robotic devices for complex tasks that may be useful in daily life. In this review, we provide an overview of the general BCI framework as well as the various methods that can be used to record neural activity, extract signals of interest, and decode brain states. In this context, we summarize the current state-of-the-art of non-invasive BCI research, focusing on trends in both the application of BCIs for controlling external devices and algorithm development to optimize their use. We also discuss various open-source BCI toolboxes and software, and describe their impact on the field at large.
KW - BCI
KW - brain-computer interface
KW - deep learning
KW - electroencephalography
KW - manifold classification
KW - motor imagery
KW - motor-related cortical potentials
KW - neural decoding
KW - neurotechnology
KW - robotic arm
KW - transfer learning
UR - https://www.scopus.com/pages/publications/85202729402
U2 - 10.1109/RBME.2024.3449790
DO - 10.1109/RBME.2024.3449790
M3 - Article
C2 - 39186407
AN - SCOPUS:85202729402
SN - 1937-3333
VL - 18
SP - 26
EP - 49
JO - IEEE Reviews in Biomedical Engineering
JF - IEEE Reviews in Biomedical Engineering
ER -