Review of Non-invasive Brain–Computer Interfaces for Continuous Motor Control

XU Minpeng; JIA Leyi; ZHOU Xiaoyu; CHEN Enze; WANG Junyang; XIAO Xiaolin; MING Dong

doi:10.11999/JEIT260011

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XU Minpeng, JIA Leyi, ZHOU Xiaoyu, CHEN Enze, WANG Junyang, XIAO Xiaolin, MING Dong. Review of Non-invasive Brain–Computer Interfaces for Continuous Motor Control[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260011

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XU Minpeng, JIA Leyi, ZHOU Xiaoyu, CHEN Enze, WANG Junyang, XIAO Xiaolin, MING Dong. Review of Non-invasive Brain–Computer Interfaces for Continuous Motor Control[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260011

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Review of Non-invasive Brain–Computer Interfaces for Continuous Motor Control

doi: 10.11999/JEIT260011 cstr: 32379.14.JEIT260011

XU Minpeng^{1, 2},
JIA Leyi²,
ZHOU Xiaoyu²,
CHEN Enze²,
WANG Junyang²,
XIAO Xiaolin^{1, 2
,
,},
MING Dong^{1, 2}

1.
Medical school, Tianjin University, Tianjin 300072, China
2.
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China

Funds: The National Natural Science Foundation of China (82330064)

Accepted Date: 2026-03-16
Rev Recd Date: 2026-03-16

Available Online: 2026-04-06

Abstract

Abstract

Significance Continuous motor control is a fundamental capability for brain–computer interface (BCI) systems aiming at natural and efficient interaction with external devices. Compared with discrete command-based control, continuous control enables real-time and smooth regulation of motion parameters such as position, velocity, and trajectory, which is essential for applications including assistive mobility, neuro rehabilitation, robotic manipulation, and immersive human–machine interaction. Although invasive BCI s have demonstrated high-performance continuous control benefiting from high-quality neural recordings, their reliance on surgical implantation restricts long-term use and large-scale deployment. Therefore, a systematic review of non-invasive continuous motor control BCI technologies is necessary to clarify research progress, methodological characteristics, and remaining challenges. Progress This review summarizes advances in non-invasive continuous motor control BCIs from four closely related aspects: control paradigms, decoding algorithms, application scenarios, and performance evaluation. At the paradigm level, motor imagery, steady-state visual evoked potentials, event-related potentials, and hybrid paradigms have been investigated to support continuous control through sustained intention modulation, dynamic stimulus encoding, and hierarchical or shared-control strategies. Regarding decoding algorithms, two major frameworks are identified: motion parameter mapping methods and motion parameter regression methods. Motion parameter mapping methods achieve continuous output by temporally integrating discrete classification results or mapping them to velocity or state variables, whereas motion parameter regression methods directly establish relationships between EEG features and continuous kinematic parameters. In recent studies, nonlinear models and deep learning approaches have been increasingly incorporated to improve robustness under non-stationary EEG conditions. At the application level, non-invasive continuous control has evolved from two-dimensional cursor tasks to more practical scenarios such as wheelchair navigation, robotic arm manipulation, unmanned systems, and virtual or augmented reality environments. In addition, existing studies evaluate continuous control performance using both objective metrics (e.g., trajectory error, task success rate, and information transfer rate) and subjective measures (e.g., workload and user experience), reflecting diverse experimental designs and control objectives. Conclusions Overall, existing studies demonstrate that non-invasive BCIs are capable of supporting continuous motor control; however, current research remains at a stage where diverse methods coexist without a unified framework. At the paradigm level, different approaches vary in their ability to reliably elicit and sustain continuous motor intentions. In terms of decoding algorithms, both motion parameter mapping and regression methods face limitations in robustness, generalization, and long-term stability due to the non-stationary nature of EEG signals. At the application level, many studies are still constrained to specific tasks and controlled environments, and the transferability of continuous control strategies to complex real-world scenarios requires further validation. Moreover, the lack of standardized evaluation protocols hinders direct comparison and systematic optimization across studies. Prospects Future research should focus on improving the stability and reliability of continuous control paradigms, enhancing decoding robustness under realistic EEG conditions, and strengthening the alignment between control strategies and application requirements. Establishing unified evaluation frameworks that integrate both objective and subjective indicators will be critical for methodological convergence and fair comparison. With continued advances, non-invasive continuous motor control BCIs are expected to play an increasingly important role in assistive technologies, rehabilitation systems, and advanced human–machine interaction.

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References(70)

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