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)

Received Date: 2026-01-05
Accepted Date: 2026-03-16
Rev Recd Date: 2026-03-16

Available Online: 2026-04-06

Abstract

Abstract

Significance Continuous motor control is a core capability of Brain-Computer Interface (BCI) systems for natural and efficient interaction with external devices. Compared with discrete command-based control, continuous control supports real-time and smooth regulation of motion parameters such as position, velocity, and trajectory. This capability is required for applications in assistive mobility, neurorehabilitation, robotic manipulation, and immersive human-machine interaction. Although invasive BCIs have achieved high-performance continuous control through high-quality neural recordings, their dependence on surgical implantation limits long-term use and large-scale deployment. A systematic review of non-invasive continuous motor control BCI technologies is therefore needed to clarify research progress, methodological features, and remaining challenges. Progress Advances in non-invasive continuous motor control BCIs are reviewed from four closely related aspects: control paradigms, decoding algorithms, applications, and performance evaluation. At the paradigm level, motor imagery, steady-state visual evoked potentials, P300, and hybrid paradigms have been studied to support continuous control through sustained intention modulation, dynamic stimulus encoding, and hierarchical or shared-control strategies. For decoding algorithms, two main frameworks are identified: motion parameter mapping and motion parameter regression. Motion parameter mapping generates continuous output by temporally integrating discrete classification results or mapping them to velocity or state variables, whereas motion parameter regression directly establishes relationships between Electroencephalogram (EEG) features and continuous kinematic parameters. Recent studies increasingly incorporate nonlinear models and deep learning methods to improve robustness under the non-stationary nature of EEG signals. At the application level, non-invasive continuous control has progressed from two-dimensional cursor tasks to more practical scenarios, including wheelchair navigation, robotic arm manipulation, unmanned systems, and virtual or augmented reality environments. Existing studies also assess continuous control performance using both objective and subjective indicators, including trajectory error, task success rate, information transfer rate, workload, and user experience, reflecting varied experimental designs and control aims. Conclusions Existing studies show that non-invasive BCIs can support continuous motor control. However, current research remains at a stage in which multiple methods coexist without a unified framework. At the paradigm level, available approaches differ in their ability to elicit and sustain continuous motor intention reliably. For decoding algorithms, both motion parameter mapping and motion parameter regression are limited by the non-stationary nature of EEG signals, which affects robustness, generalization, and long-term stability. At the application level, many studies remain restricted to specific tasks and controlled environments, and the transfer of continuous control strategies to complex real-world scenarios still requires further validation. Moreover, the lack of standardized evaluation protocols hinders direct comparison and systematic optimization across studies. Prospects Future research should improve the stability and reliability of continuous control paradigms, enhance decoding robustness under realistic EEG conditions, and strengthen the match between control strategies and application requirements. Unified evaluation frameworks that integrate objective and subjective indicators should also be established to support methodological convergence and fair comparison. With continued progress, non-invasive continuous motor control BCIs are expected to play a growing role in assistive technologies, rehabilitation systems, and advanced human-machine interaction.
- Brain-Computer Interface (BCI),
- Electroencephalogram (EEG),
- Continuous motor control

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References(70)

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