Implementation of a Brain-Computer Interface on a Lower-Limb Exoskeleton
Open accessAbstract
In this paper, we propose to use brain-computer interface (BCI) to control a lower-limb exoskeleton. The exoskeleton follows the wearer's motion intention through decoding of electroencephalography (EEG) signals and multi-modal cognition. Motion patterns as standing up, sitting down, and walking forward can be performed. We implemented two types of BCIs, one based on steady-state visual evoked potentials, which used canonical correlation analysis to extract the frequency the subject focused on. The other BCI is based on motor imagery, where the common spatial patterns method was employed to extract the features from the EEG signal. Then, the features were classified by support vector machine to recognize the intention of the subject. We invited four healthy subjects to participate in the experiments, including off-line and online. The off-line experiments trained the classifier and then were used online to test the performance of the BCI controlled exoskeleton system. The results showed high accuracy rate in motion intention classification tasks for both BCIs.
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Common Spatial Pattern (CSP) performs spatial filtering maximizing inter-class variance differences, enhances EEG signal separability, and is used for EEG motor imagery classification.
“| Common Spatial | Spatial filtering maximizing inter-class | Enhance EEG signal separability | EEG motor imagery classification 139,260 |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [260]
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Electromyography-controlled lower extremity exoskeleton to provide wearers flexibility in walking
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“Notable examples of BCI application include controlling of wheelchair (Zhang et al., 2015), exoskeleton (Wang et al., 2018), speller (Rezeika et al., 2018), gaming (Ahn et al., 2014), etc.”
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Current Status and Consideration of Support/Care Robots for Stand-Up Motion
Nakamura, K., & Saga, N. (2021) · Applied Sciences · doi:10.3390/app11041711 · Cited by 5 · see citing works
Development of A Non-Power Waist Assist Device and IEMG-Based Evaluation of Assist Effect
Ya, Z., Peng, A., He, Y., et al. (2019) · IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm.2019.8834080 · Cited by 5 · see citing works
“Clinical studies have shown that vertebral muscle injury [13] is the main cause of pain in the back and waist.”
BackgroundSelf-citationMa, Y., Zheng, L., Yi, Z., Xiao, Y., Wang, C., & Wu, X. (2021) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar52367.2021.9517461 · Cited by 5 · see citing works
Self-citationEEG2GAIT: A Hierarchical Graph Convolutional Network for EEG-Based Gait Decoding
Fu, X., Liu, R., Wai, A. A. P., Pulferer, H. S., Robinson, N., Muller-Putz, G. R., & Guan, C. (2025) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2025.3647101 · Cited by 4 · see citing works
“Finally, Wang and colleagues [28] developed a real-time BCI system for lower-limb exoskeleton control, demonstrating EEG’s feasibility in rehabilitation.”
Oscillator-Based Hybrid Gait Phase Estimation Method for Hip Assistive Exoskeleton
Tan, M.-H., Wu, Q., He, Z., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3219093 · Cited by 4 · see citing works
Wang, D., Wu, Y., & Yu, H. (2024) · Brain Connectivity · doi:10.1089/brain.2024.0005 · Cited by 4 · see citing works
“12) Single Task-MIT-sit-to-stand/stand-to-sit: This lower limb task has been involved in only one article (Wang et al., 2018), spanning seven different brain region types.”
InfluentialSignal Decomposition-Based MEG Analysis for Motor and Cognitive Imagery Classification
Koç, G., Yousif, M. A. A., & Öztürk, M. (2025) · Electronics · doi:10.3390/electronics14173424 · Cited by 3 · see citing works
Deep Learning Brain Actuated Bidirectional Communication and Rehabilitation aid for the Quadriplegic
Jacob, S., Alagirisamy, M., Vinoj, P. G., et al. (2021) · International Conference on Secure Cyber Computing and Communications (ICSCCC) · doi:10.1109/icsccc51823.2021.9478146 · Cited by 3 · see citing works
“Lately, Brain-Computer Interface (BCI) were utilized to command exoskeletons [4].”
MethodMotor Imagination of Lower Limb Movements at Different Frequencies
Liu, Y., Chen, C., Belkacem, A. N., et al. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/4073739 · Cited by 3 · see citing works
Self-citationA New EEG-based Paradigm for Classifying Intention of Compound-Limbs Movement
Ma, R., Jiang, Y., Chen, Y., et al. (2022) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar54675.2022.9872213 · Cited by 3 · see citing works
Wu, Y., Li, J., Ren, Z., et al. (2023) · Chinese Control Conference (CCC) · doi:10.23919/ccc58697.2023.10239884 · Cited by 3 · see citing works
Functional Electrical Stimulation with Machine Learning for Stroke Prediction and Rehabilitation
Suresh, A., Abhishek, R. S., Akash, P., et al. (2024) · doi:10.1109/icemps60684.2024.10559321 · Cited by 3 · see citing works
Li, L.-L., Cao, G., Zhang, Y.-P., et al. (2024) · Sensors · doi:10.3390/s24237611 · Cited by 3 · see citing works
Brain computer interfaces for communication, mobility and motor recovery
Tabernig, C. B., Carrere, L. C., Atum, Y. V., & Acevedo, R. (2025) · Elsevier eBooks · doi:10.1016/b978-0-12-819877-3.00006-8 · Cited by 3 · see citing works
Padfield, N., Camilleri, T. A., Fabri, S. G., Bugeja, M. K., & Camilleri, K. P. (2024) · Brain-Computer Interfaces · doi:10.1080/2326263x.2024.2379009 · Cited by 2 · see citing works
Novel Lower Limb Exoskeleton Rehabilitation System Based on Tactile Sensors and Digital Twin
Lee, M.-C., & Pan, C. (2025) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc62753.2025.11079160 · Cited by 2 · see citing works
Roy, G., & Bhaumik, S. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-7011-4_25 · Cited by 2 · see citing works
Electroencephalogram-based brain–computer interface systems for controlling rehabilitative devices
Tarafdar, K. K., Pradhan, B. K., Nayak, S. K., Khasnobish, A., Bhattacharyya, S., & Pal, K. (2019) · Elsevier eBooks · doi:10.1016/b978-0-08-102420-1.00041-8 · Cited by 2 · see citing works
Channel Synergy-based Human-Robot Interface for a Lower Limb Walking Assistance Exoskeleton
Shi, K., Huang, R., Mu, F., et al. (2021) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc46164.2021.9631040 · Cited by 2 · see citing works
“It can directly obtain patients’ motion intention from the EEG signal and without actual limb movement, so the BCI has been used to predict the movement of paraplegic patients [8-10].”
MethodA Smart System to Classify Walking and Sitting Activities Based on EEG Signal
Bhatlawande, S. S., Shilaskar, S., Kamathe, A., et al. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-1414-2_2 · Cited by 1 · see citing works
Jiang, Y., Cao, J., Niu, X., et al. (2025) · Ceramics International · doi:10.1016/j.ceramint.2025.09.367 · Cited by 1 · see citing works
EEG-to-gait decoding via phase-aware representation learning
Fu, X., Jiang, W., Liu, R., Muller-Putz, G. R., & Guan, C. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.108608 · Cited by 1 · see citing works
Rehabilitative use of fNIRS controlled lower limb exoskeleton
Ullah, R. S., Nazeer, H., Rehman, H. Z. U., et al. (2023) · International Conference on Digital Futures and Transformative Technologies (ICoDT2) · doi:10.1109/icodt259378.2023.10325740 · Cited by 1 · see citing works
“Motor Imagery and Motor Execution are two BCIs that are widely employed[15], [16].”
BackgroundLower Limb Exoskeletons and Virtual Reality for Rehabilitation: A Technological Review
Huamanchahua, D., Abad, J., Aique, R., et al. (2023) · IEEE International Conference on Industry Applications · doi:10.1109/induscon58041.2023.10374880 · Cited by 1 · see citing works
“Pressure sensors can be used to calculate the pressure and its distribution between the ground and the feet of a patient [31] [32] [42].”
BackgroundBrain-Controlled Assistive Robotics and Prosthetics
Puthuveetil, K. P., & Krusienski, D. J. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-87865-4.00010-8 · Cited by 1 · see citing works
Tendon-based design of wrist joint for tongue-controlled exoskeleton - a case study
Vestersoe, K. B. P., Mohammadi, M., Kromann, E. B., et al. (2021) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe52308.2021.9635558 · Cited by 1 · see citing works
“Other control interfaces for assistive and rehabilitation exoskeletons are based on electromyography (EMG) [7,14,15], or electroencephalography (EEG) [16,17,18], or other forms of brain-computer interfacing [19,20].”
BackgroundInfluentialPourmand, M. M., Tamimi, H. A., Sarfarazi, S. M., Kardan, I., & Akbarzadeh, A. (2022) · RSI International Conference on Robotics and Mechatronics (ICRoM) · doi:10.1109/icrom57054.2022.10025061 · Cited by 1 · see citing works
“These devices include wearable lower extremity exoskeletons[2].”
Analysis and design of a flexible exoskeleton for the upper limbs
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Data-efficient human walking speed intent identification
Higgins, T. M., Bresingham, K. J., Schmiedeler, J. P., et al. (2023) · Wearable Technologies · doi:10.1017/wtc.2023.15
“moving (Bi et al., 2013; Wang et al., 2018; Vinoj et al., 2019), EEG does not require true motor control over the remaining physiology.”
BackgroundEEG Signals Decoding of Freely Moving Rats Based on Support Vector Machine Algorithm
Kou, J., Sun, C., Shi, Y., Yang, L., Niu, Y., Yang, Z., Nie, Y., & Chang, J. (2024) · Asia Conference on Information Engineering (ACIE) · doi:10.1109/acie61839.2024.00023
“To this end, researchers have proposed an active control method [7] and introduced brain-computer interface technology [8].”
MethodA High-Performance Classification Method Based on CNN for Interventional EEG Data
Li, S., Liu, G., Feng, F., Wu, D., Ma, Y., & Duan, F. (2024) · International Symposium on Autonomous Systems (ISAS) · doi:10.1109/isas61044.2024.10552482
“Furthermore, Wang et al. implanted electrodes into the hand region of the sensorimotor cortex, the patients wore lower limb exoskeletons and drove it by upper limb motor imagery from BCI [16].”
MethodSelf-citationGuo, W., Katiyar, S. A., Davis, S., et al. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5067506
AI assisted BCI system for rehabilitation
Vinoj, P. G., & Menon, V. G. (2025) · International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) · doi:10.1109/access65134.2025.11135765
“However, recent developments have shifted focus toward controlling exoskeletons using BCI [16-17].”
End-to-End Decoding of Motion Intention from fNIRS Signals Using Deep Learning Framework
Kong, J., Zhu, C. J., Hou, F., et al. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.10.113
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Lee, M.-C., Chen, C.-C., & Pan, C. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3654715
“Wang et al. [28] proposed a BCI-controlled exoskeleton capable of detecting movement intent through neural signal decoding.”
Mohammed, K. K., Afify, H. M., & Hassanien, A. E. (2026) · Journal of Bionic Engineering · doi:10.1007/s42235-026-00869-y
Bhambhani, Y., Ortíz, M., Iáñez, E., Diaz, J. A., Romero, J. O. R., & Azorín, J. M. (2026) · Applied Sciences · doi:10.3390/app16168191
Ma, J., Wu, J., Sun, C., & Liang, J. (2026) · Mechanism and Machine Theory · doi:10.1016/j.mechmachtheory.2026.106574
- Authors
- Wang, C., Wu, X., Wang, Z., & Ma, Y.
- Affiliations
- Chinese Academy of Sciences (CN)
- University of Chinese Academy of Sciences (CN)
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 38524-38534
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2861373854
- Semantic Scholar
- 2a1d6e540b1375c94597b38dd365d725950781a7
- Cited by
- 89
- References
- 33
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 12 · 2023: 13 · 2022: 15 · 2021: 17 · 2020: 6 · 2019: 11
- FWCI
- 5.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Key Research and Development Program of China (2017YFB1302303)
- National Natural Science Foundation–Shenzhen Joint Research Program (U1613219)
- State Joint Engineering Laboratory for Robotics and Intelligent Manufacturing through the National Development and Reform Commission (2015581)
- Shenzhen Engineering Laboratory for Robotics and Intelligent Manufacturing through the Shenzhen Municipal Development and Reform Commission (20141722)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Keywords
- brain-computer interface, lower limb exoskeleton, EEG decoding, steady-state visual evoked potentials, motor imagery, common spatial pattern, support vector machine, assistive robotics
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