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Brain-machine interaction and assist effect evaluation of a single-degree-of-freedom sit-to-stand transfer robot
Published online by Cambridge University Press: 11 July 2025
Abstract
In response to the prevailing trend of an aging society and the increasing requirements of rehabilitation, this paper presents an approach involving brain-machine interaction (BMI) for a single-degree-of-freedom (1-DOF) sit-to-stand transfer robot. Based on a 1-DOF rehabilitation robot, three experiment paradigms involving motor imagery (MI), action observation of motor imagery (AO-MI) and motor execution are designed using both electroencephalography (EEG) and electromyography (EMG). To enhance motion intention recognition accuracy, a Gumbel-ResNet-KANs decoding model is established. The Gumbel-ResNet-KANs model integrates the Gumbel-Softmax method with the ResNet-KANs network module and demonstrates strong decoding capability, as demonstrated by comparative tests in this paper. To validate the effect of robotic assistance, EEG and EMG coherence are analyzed to assess the impact of robotic assistance on rehabilitation from a neuromuscular perspective in both assisted and unassisted conditions. We assessed the effect of robotics on rehabilitation from an emotional perspective by analyzing the difference between the differential entropy of the right and left brain. The proposed study also reveals that the movement-related cortical potentials in AO-MI are beneficial for promoting the performance of BMI in sit-to-stand training, which provides a possible approach for the development of new types of robots for lower limb rehabilitation.
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- © The Author(s), 2025. Published by Cambridge University Press
References
Li, H., Huang, G., Lin, Q., Zhao, J.-L., Lo, W.-L. A., Mao, Y.-R., Chen, L., Zhang, Z.-G., Huang, D.-F. and Li, L., “Combining movement-related cortical potentials and event-related desynchronization to study movement preparation and execution,” Front. Neurol. 9, 822 (2018).CrossRefGoogle ScholarPubMed
Laaksonen, K., Kirveskari, E., Mäkelä, J. P., Kaste, M., Mustanoja, S., Nummenmaa, L., Tatlisumak, T. and Forss, N., “Effect of afferent input on motor cortex excitability during stroke recovery,” Clin. Neurophysiol. 123(12), 2429–2436 (2012).CrossRefGoogle ScholarPubMed
Cheng, P.-T., Chen, C.-L., Wang, C.-M. and Hong, W.-H., “Leg muscle activation patterns of sit-to-stand movement in stroke patients,” Am. J. Phys. Med. Rehabil. 83(1), 10–16 (2004).CrossRefGoogle ScholarPubMed
Maggioni, S., Melendez-Calderon, A., Van Asseldonk, E., Klamroth-Marganska, V., Lünenburger, L., Riener, R. and Van Der Kooij, H., “Robot-aided assessment of lower extremity functions: A review,” J. Neuroeng. Rehabil. 13(1), 1–25 (2016).CrossRefGoogle ScholarPubMed
Zhao, P., Zhang, Y., Guan, H., Deng, X. and Chen, H., “Design of a single-degree-of-freedom immersive rehabilitation device for clustered upper-limb motion,” J. Mech. Robot. 13(3), 031006 (2021).CrossRefGoogle Scholar
Kapsalyamov, A., Hussain, S., Goecke, R., Brown, N. A. and Jamwal, P. K., “Customized stiffness control strategy for a six-bar linkage-based gait rehabilitation robot,” Robotica 42(10), 3398–3415 (2024).CrossRefGoogle Scholar
Cao, Q., Li, L., Li, J., Li, R. and Wang, X., “A methodology to quantify human-robot interaction forces: A case study of a 4-dofs upper extremity rehabilitation robot,” Robotica 1-22, 1–22 (2025).Google Scholar
Shin, J.-h., Byeon, N., Yu, H., Yun, G., Kim, H., Lim, S., Kim, D., Lee, H.-J. and Lee, W.-h., “Effect of 4-weeks exercise program using wearable hip-assist robot (ex1) in older adults: one group pre-and post-test,” BMC Geriatr. 23(1), 724 (2023).CrossRefGoogle ScholarPubMed
Wendong, W., Hanhao, L., Menghan, X., Yang, C., Xiaoqing, Y., Xing, M. and Bing, Z., “Design and verification of a human–robot interaction system for upper limb exoskeleton rehabilitation,” Med. Eng. Phys. 79, 19–25 (2020).CrossRefGoogle ScholarPubMed
Takahashi, K., Mizukami, M., Watanabe, H., Kuroda, M. M., Shimizu, Y., Nakajima, T., Mutsuzaki, H., Kamada, H., Tokeji, K., Hada, Y., Koseki, K., Yoshikawa, K., Nakayama, T., Iwasaki, N., Kawamoto, H., Sankai, Y., Yamazaki, M., Matsumura, A. and Marushima, A., “Feasibility and safety study of wearable cyborg hybrid assistive limb for pediatric patients with cerebral palsy and spinal cord disorders,” Front. Neurol. 14, 1255620 (2023).CrossRefGoogle ScholarPubMed
Bhardwaj, S., Khan, A. A. and Muzammil, M., “Lower limb rehabilitation robotics: the current understanding and technology,” Work 69(3), 775–793 (2021).Google ScholarPubMed
Wu, L., Xu, G. and Wu, Q., “The effect of the lokomat® robotic-orthosis system on lower extremity rehabilitation in patients with stroke: a systematic review and meta-analysis,” Front. Neurol. 14, 1260652 (2023).CrossRefGoogle ScholarPubMed
Lee, S.-H., Kim, J., Lim, B., Lee, H.-J. and Kim, Y.-H., “Exercise with a wearable hip-assist robot improved physical function and walking efficiency in older adults,” Sci. Rep-UK 13(1), 7269 (2023).CrossRefGoogle ScholarPubMed
Siviy, C., Baker, L. M., Quinlivan, B. T., Porciuncula, F., Swaminathan, K., Awad, L. N. and Walsh, C. J., “Opportunities and challenges in the development of exoskeletons for locomotor assistance,” Nat. Biomed. Eng. 7(4), 456–472 (2023).CrossRefGoogle ScholarPubMed
Swank, C., Trammell, M., Bennett, M., Ochoa, C., Callender, L., Sikka, S. and Driver, S., “The utilization of an overground robotic exoskeleton for gait training during inpatient rehabilitation—single-center retrospective findings,” Int. J. Rehabil. Res. 43(3), 206–213 (2020).CrossRefGoogle ScholarPubMed
Wu, Q. and Chen, Y., “Adaptive cooperative control of a soft elbow rehabilitation exoskeleton based on improved joint torque estimation,” Mech. Syst. Signal Pr. 184, 109748 (2023).CrossRefGoogle Scholar
Liu, H.-H., Wang, R.-Y., Cheng, S.-J., Liao, K.-K., Zhou, J.-H. and Yang, Y.-R., “Balance training modulates cortical inhibition in individuals with parkinson’s disease: a randomized controlled trial,” Neurorehab. Neural. Re. 36(9), 613–620 (2022).CrossRefGoogle ScholarPubMed
Canales-Díaz, M. B., Olivares-Valenzuela, C., Ramírez-Arriagada, A., Cruz-Montecinos, C., Vilaró, J., Torres-Castro, R. and Núñez-Cortés, R., “Clinical effects of rehabilitation on balance in people with chronic obstructive pulmonary disease: a systematic review and meta-analysis,” Front. Med. 9, 868316 (2022).CrossRefGoogle ScholarPubMed
Albanese, G. A., Taglione, E., Gasparini, C., Grandi, S., Pettinelli, F., Sardelli, C., Catitti, P., Sandini, G., Masia, L. and Zenzeri, J., “Efficacy of wrist robot-aided orthopedic rehabilitation: a randomized controlled trial,” J. Neuroeng. Rehabil. 18(1), 130 (2021).CrossRefGoogle ScholarPubMed
Takahashi, A., Sato, M. and Namiki, A.. Dynamic Compensation in Throwing Motion with High-Speed Robot Hand-Arm. In: 2021 IEEE International Conference on Robotics and Automation (ICRA) (IEEE, 2021) pp. 6287–6292.CrossRefGoogle Scholar
Ketkar, V. D., Wolbrecht, E. T., Perry, J. C. and Farrens, A., “Design and development of a spherical 5-bar thumb exoskeleton mechanism for poststroke rehabilitation,” J. Med. Dev. 17(2), 021002 (2023).CrossRefGoogle ScholarPubMed
Wang, Y.-l., Wang, K.-y., Chai, Y.-j., Mo, Z.-j. and Wang, K.-c., “Research on mechanical optimization methods of cable-driven lower limb rehabilitation robot,” Robotica 40(1), 154–169 (2022).CrossRefGoogle Scholar
Wang, Y.-L., Wang, K.-Y., Wang, W.-L., Han, Z. and Zhang, Z.-X., “Appraisement and analysis of dynamical stability of under-constrained cable-driven lower-limb rehabilitation training robot,” Robotica 39(6), 1023–1036 (2021).CrossRefGoogle Scholar
Zhang, L., Guo, S. and Sun, Q., “An assist-as-needed controller for passive, assistant, active, and resistive robot-aided rehabilitation training of the upper extremity,” Appl. Sci. 11(1), 340 (2020).CrossRefGoogle Scholar
Lin, Y., Qu, Q., Lin, Y., He, J., Zhang, Q., Wang, C., Jiang, Z., Guo, F. and Jia, J., “Customizing robot-assisted passive neurorehabilitation exercise based on teaching training mechanism,” Biomed. Res. Int. 2021(1), 9972560 (2021).CrossRefGoogle ScholarPubMed
Hu, J., Hou, Z.-G., Chen, Y.-X., Zhang, F. and Wang, W.-Q., “Lower limb rehabilitation robots and interactive control methods,” Acta Automat. Sin. 40(11), 2377–2390 (2014).Google Scholar
Zhao, D., Sun, X., Shan, B., Yang, Z., Yang, J., Liu, H., Jiang, Y. and Hiroshi, Y., “Research status of elderly-care robots and safe human-robot interaction methods,” Front. Neurosci.-SWITZ 17, 1291682 (2023).CrossRefGoogle ScholarPubMed
Mane, R., Chouhan, T. and Guan, C., “Bci for stroke rehabilitation: Motor and beyond,” J. Neural Eng. 17(4), 041001 (2020).CrossRefGoogle ScholarPubMed
Zhou, J., Yang, S. and Xue, Q., “Lower limb rehabilitation exoskeleton robot: a review,” Adv. Mech. Eng. 13(4), 16878140211011862 (2021).CrossRefGoogle Scholar
Camargo-Vargas, D., Callejas-Cuervo, M. and Mazzoleni, S., “Brain-computer interfaces systems for upper and lower limb rehabilitation: A systematic review,” Sensors 21(13), 4312 (2021).CrossRefGoogle ScholarPubMed
Cui, Z., Li, Y., Huang, S., Wu, X., Fu, X., Liu, F., Wan, X., Wang, X., Zhang, Y., Qiu, H., Chen, F., Yang, P., Zhu, S., Li, J. and Chen, W., “Bci system with lower-limb robot improves rehabilitation in spinal cord injury patients through short-term training: a pilot study,” Cogn. Neurodyn. 16(6), 1283–1301 (2022).CrossRefGoogle ScholarPubMed
Zhu, C., Maurya, S., Yi, J. and Dutta, A.. Brain Computer Interface (bci)-Enhanced Knee Exoskeleton Control for Assisted Sit-to-Stand Movement. In: 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM) (IEEE, 2024) pp. 278–283.CrossRefGoogle Scholar
Zhang, X., Rong, X. and Luo, H., “Optimizing lower limb rehabilitation: The intersection of machine learning and rehabilitative robotics,” Front. Rehabil. Sci. 5, 1246773 (2024).CrossRefGoogle ScholarPubMed
Chaisaen, R., Autthasan, P., Mingchinda, N., Leelaarporn, P., Kunaseth, N., Tammajarung, S., Manoonpong, P., Mukhopadhyay, S. C. and Wilaiprasitporn, T., “Decoding eeg rhythms during action observation, motor imagery, and execution for standing and sitting,” IEEE Sens. J. 20(22), 13776–13786 (2020).CrossRefGoogle Scholar
Khan, M. A., Das, R., Iversen, H. K. and Puthusserypady, S., “Review on motor imagery based bci systems for upper limb post-stroke neurorehabilitation: From designing to application,” Comput. Biol. Med. 123, 103843 (2020).CrossRefGoogle ScholarPubMed
Ghani, F., Sultan, H., Anwar, D., Farooq, O. and Khan, Y. U., “Classification of wrist movements using eeg signals,” J. Next Gener. Inf. Technol. (JNIT) 4(8), 29–39 (2013).Google Scholar
Gomez-Rodriguez, M., Peters, J., Hill, J., Schölkopf, B., Gharabaghi, A. and Grosse-Wentrup, M., “Closing the sensorimotor loop: Haptic feedback facilitates decoding of motor imagery,” J. Neural Eng. 8(3), 036005 (2011).CrossRefGoogle ScholarPubMed
Webb, J., Xiao, Z. G., Aschenbrenner, K. P., Herrnstadt, G. and Menon, C.. Towards a Portable Assistive Arm Exoskeleton for Stroke Patient Rehabilitation Controlled Through a Brain Computer Interface. In: 2012, 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), IEEE (2012) pp. 1299–1304.Google Scholar
Bulea, T. C., Prasad, S., Kilicarslan, A. and Contreras-Vidal, J. L., “Sitting and standing intention can be decoded from scalp eeg recorded prior to movement execution,” Front. Neurosci.-SWITZ 8, 376 (2014).Google ScholarPubMed
Jeong, J.-H., Shim, K.-H., Kim, D.-J. and Lee, S.-W.. Trajectory Decoding of Arm Reaching Movement Imageries for Brain-Controlled Robot Arm System. In: 2019, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2019) pp. 5544–5547.CrossRefGoogle Scholar
Liu, D., Chen, W., Lee, K., Chavarriaga, R., Iwane, F., Bouri, M., Pei, Z. and d. R. Millán, J., “Eeg-based lower-limb movement onset decoding: Continuous classification and asynchronous detection,” IEEE Trans Neur Sys. Reh. 26(8), 1626–1635 (2018).CrossRefGoogle ScholarPubMed
Temporiti, F., Calcagno, A., Coelli, S., Marino, G., Gatti, R., Bianchi, A. M. and Galli, M., “Early sleep after action observation and motor imagery training boosts improvements in manual dexterity,” Sci. Rep.-UK 13(1), 2609 (2023).CrossRefGoogle ScholarPubMed
Zhang, X., Yao, L., Wang, X., Monaghan, J., Mcalpine, D. and Zhang, Y., “A survey on deep learning based brain computer interface: Recent advances and new frontiers,” J. Neural Eng. 18(3), 031002 (2021).CrossRefGoogle ScholarPubMed
Craik, A., He, Y. and Contreras-Vidal, J. L., “Deep learning for electroencephalogram (eeg) classification tasks: A review,” J. Neural Eng. 16(3), 031001 (2019).CrossRefGoogle ScholarPubMed
Sturm, I., Lapuschkin, S., Samek, W. and Müller, K.-R., “Interpretable deep neural networks for single-trial eeg classification,” J. Neurosci. Meth. 274, 141–145 (2016).CrossRefGoogle ScholarPubMed
Freitas, D. R., Inocêncio, A. V., Lins, L. T., Santos, E. A. and Benedetti, M. A., “A Real-Time Embedded System Design for Erd/Ers Measurement On Eeg-Based Brain-Computer Interfaces,” In: XXVI Brazilian Congress On Biomedical Engineering: CBEB. 2018, Armação De Buzios, RJ, Brazil, 21–25 October 2018, vol. 2 (Springer, 2019) pp. 25–33.CrossRefGoogle Scholar
Torres, J. M. M., Medina-DeVilliers, S., Clarkson, T., Lerner, M. D. and Riccardi, G., “Evaluation of interpretability for deep learning algorithms in eeg emotion recognition: A case study in autism,” Artif. Intell. Med. 143, 102545 (2023).CrossRefGoogle Scholar
Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Soljačić, M., Hou, T. Y. and Tegmark, M., “KAN: Kolmogorov–Arnold networks,” In: The Thirteenth International Conference on Learning Representations (2025). https://openreview.net/forum?id=Ozo7qJ5vZi.Google Scholar
Han, X., Zhang, X., Wu, Y., Zhang, Z. and Wu, Z., “Kan4tsf: Are kan and kan-based models effective for time series forecasting? (2024). https://arxiv.org/html/2408.11306v1.Google Scholar
Contreras, L. F. H., Cui, J., Yu, L., Huang, Z., Nikpour, A. and Kavehei, O., “KAN–EEG: Towards replacing backbone–MLP for an effective seizure detection system,” R. Soc. Open Sci. 12(3), 240999 (2025).CrossRefGoogle Scholar
He, K., Zhang, X., Ren, S. and Sun, J.. Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016) pp. 770–778.Google Scholar
Tian, T., Wang, L., Luo, M. and Zhu, W., “A novel psychotherapy effect detector of public art based on resnet and eeg imaging,” Comput. Math. Method. Med. 2022(1), 4909294–10 (2022).CrossRefGoogle ScholarPubMed
Sun, Y., Xiao, C., Chen, L., Chen, L., Lu, H., Wang, Y., Zheng, Y., Zhang, Z. and Xiong, R., “A review of intelligent walking support robots: Aiding sit-to-stand transition and walking,” IEEE Trans. Neur. Sys. Reh. 32, 1355–1369 (2024).CrossRefGoogle ScholarPubMed
Renton, T., Tibbles, A. and Topolovec-Vranic, J., “Neurofeedback as a form of cognitive rehabilitation therapy following stroke: A systematic review,” PLoS One 12(5), e0177290 (2017).CrossRefGoogle ScholarPubMed
Foong, R., Ang, K. K., Quek, C., Guan, C., Phua, K. S., Kuah, C. W. K., Deshmukh, V. A., Yam, L. H. L., Rajeswaran, D. K., Tang, N., Chew, E. and Chua, K. S. G., “Assessment of the efficacy of eeg-based mi-bci with visual feedback and eeg correlates of mental fatigue for upper-limb stroke rehabilitation,” IEEE Trans. Bio-MED Eng. 67(3), 786–795 (2019).CrossRefGoogle ScholarPubMed
Cheng, P.-T., Liaw, M.-Y., Wong, M.-K., Tang, F.-T., Lee, M.-Y. and Lin, P.-S., “The sit-to-stand movement in stroke patients and its correlation with falling,” Arch. Phys. Med. Rehab. 79(9), 1043–1046 (1998).CrossRefGoogle ScholarPubMed
Lecours, J., Nadeau, S., Gravel, D. and Teixera-Salmela, L., “Interactions between foot placement, trunk frontal position, weight-bearing and knee moment asymmetry at seat-off during rising from a chair in healthy controls and persons with hemiparesis,” J. Rehabil. Med. 40(3), 200–207 (2008).CrossRefGoogle ScholarPubMed
Özyürek, S., Demirbüken, İ. and Angın, S., “Altered movement strategies in sit-to-stand task in persons with transtibial amputation,” Prosthet. Orthot. Int. 38(4), 303–309 (2014).CrossRefGoogle ScholarPubMed
Lomaglio, M. J. and Eng, J. J., “Muscle strength and weight-bearing symmetry relate to sit-to-stand performance in individuals with stroke,” Gait. Posture 22(2), 126–131 (2005).CrossRefGoogle ScholarPubMed
Chen, R.-c., Li, X.-y., Guan, L.-l., Guo, B.-p., Wu, W.-l., Zhou, Z.-q., Huo, Y.-t., Chen, X. and Zhou, L.-q., “Effectiveness of neuromuscular electrical stimulation for the rehabilitation of moderate-to-severe copd: A meta-analysis,” Int. J. Chronic Obstr. 2965-2975, 2965–2975 (2016).Google Scholar
Sevilla, G. G.-P.-D. and Pinto, B. S.-P., “Effectiveness of physical exercise and neuromuscular electrical stimulation interventions for preventing and treating intensive care unit-acquired weakness: A systematic review of randomized controlled trials,” Intens. Crit. Care Nur. 74, 103333 (2023).CrossRefGoogle Scholar
Dal Corso, S., Nápolis, L., Malaguti, C., Gimenes, A. C., Albuquerque, Aé, Nogueira, C. R., De Fuccio, M. B., Pereira, R. D. B., Bulle, A., McFarlane, N., Nery, L. E. and Neder, J. A., “Skeletal muscle structure and function in response to electrical stimulation in moderately impaired copd patients,” Resp. Med. 101(6), 1236–1243 (2007).CrossRefGoogle ScholarPubMed
McFarland, D. J. and Wolpaw, J. R., “Eeg-based brain–computer interfaces,” Curr. Opin. Biomed. Eng. 4, 194–200 (2017).CrossRefGoogle ScholarPubMed
Shiraishi, R., Kawamoto, H. and Sankai, Y., “Integrated wheelchair-compatible support system for sit-to-stand movements support,” J. Med. Devices 13(4), 044501 (2019).CrossRefGoogle Scholar
Helander, M. G. and Zhang, L., “Field studies of comfort and discomfort in sitting,” Ergonomics 40(9), 895–915 (1997).CrossRefGoogle ScholarPubMed
Yan, Y. and Jia, Y., “A review on human comfort factors, measurements, and improvements in human–robot collaboration,” Sensors 22(19), 7431 (2022).CrossRefGoogle ScholarPubMed
Fernandes, J. V. M. R., d. Alexandria, A. R., Marques, J. A. L., d. Assis, D. F., Motta, P. C. and d. S. Silva, B. R., “Emotion detection from eeg signals using machine deep learning models,” Bioengineering 11(8), 782 (2024).CrossRefGoogle ScholarPubMed
Veerbeek, J. M., van Wegen, E., van Peppen, R., Van der Wees, P. J., Hendriks, E., Rietberg, M. and Kwakkel, G., “What is the evidence for physical therapy poststroke? A systematic review and meta-analysis,” PLoS One 9(2), e87987 (2014).CrossRefGoogle Scholar
Li, X., Lv, H., Zhao, P. and Lu, Q., “A fourier approach to kinematic acquisition of geometric constraints of planar motion for practical mechanism design,” J. Mech. Design 144(12), 123302 (2022).CrossRefGoogle Scholar
Chen, P., Dong, D., Lv, H. and Zhu, L., “A user motion data acquisition and processing method for the design of rehabilitation robot with few degrees-of-freedom,” J. Eng. Sci. Med. Diagnost. Therapy 3(2), 021104 (2020).CrossRefGoogle Scholar
Al-Saegh, A., Dawwd, S. A. and Abdul-Jabbar, J. M., “Deep learning for motor imagery eeg-based classification: A review,” Biomed. Signal Proces. 63, 102172 (2021).CrossRefGoogle Scholar
Triana-Guzman, N., Orjuela-Cañon, A. D., Jutinico, A. L., Mendoza-Montoya, O. and Antelis, J. M., “Decoding eeg rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface,” Front. Neuroinform. 16, 961089 (2022).CrossRefGoogle ScholarPubMed
Rungsirisilp, N. and Wongsawat, Y., “Applying combined action observation and motor imagery to enhance classification performance in a brain–computer interface system for stroke patients,” IEEE Access 10, 73145–73155 (2022).CrossRefGoogle Scholar
Das, S. K. and Mukhopadhyay, S., “Integrating ergonomics tools in physical therapy for musculoskeletal risk assessment and rehabilitation – A review,” Int. J. Eng. Sci. Res. 2(10), 136–155 (2014).Google Scholar
Youssofzadeh, V., Zanotto, D., Wong-Lin, K., Agrawal, S. K. and Prasad, G., “Directed functional connectivity in fronto-centroparietal circuit correlates with motor adaptation in gait training,” IEEE Trans. Neur. Sys. Reh. 24(11), 1265–1275 (2016).CrossRefGoogle ScholarPubMed
Xie, P., Zhou, S., Chen, X., Yang, W., Zhang, L. and Hu, G., “Estimation of Corticomuscular Coherence Following Stroke Patients,” In: 2017 Chinese Automation Congress (CAC) (IEEE, 2017) pp. 4263–4266.CrossRefGoogle Scholar
Zheng, W.-L., Zhu, J.-Y. and Lu, B.-L., “Identifying stable patterns over time for emotion recognition from eeg,” IEEE Trans. Affect Comput. 10(3), 417–429 (2017).CrossRefGoogle Scholar
Harmon-Jones, E. and Allen, J. J., “Anger and frontal brain activity: Eeg asymmetry consistent with approach motivation despite negative affective valence,” J. Pers. Soc. Psychol. 74(5), 1310–1316 (1998).CrossRefGoogle ScholarPubMed
Davidson, R. J., “Eeg measures of cerebral asymmetry: Conceptual and methodological issues,” Int. J. Neurosci. 39(1-2), 71–89 (1988).CrossRefGoogle ScholarPubMed
Liu, X., Lv, L., Shen, Y., Xiong, P., Yang, J. and Liu, J., “Multiscale space-time-frequency feature-guided multitask learning cnn for motor imagery eeg classification,” J. Neural Eng. 18(2), 026003 (2021).CrossRefGoogle ScholarPubMed
Gehan, E. A., “A generalized wilcoxon test for comparing arbitrarily singly-censored samples,” Biometrika 52(1-2), 203–224 (1965).CrossRefGoogle ScholarPubMed
Altaheri, H., Muhammad, G., Alsulaiman, M., Amin, S. U., Altuwaijri, G. A., Abdul, W., Bencherif, M. A. and Faisal, M., “Deep learning techniques for classification of electroencephalogram (eeg) motor imagery (mi) signals: A review,” Neural Comput. Appl. 35(20), 14681–14722 (2023).CrossRefGoogle Scholar
Herbold, S., “Autorank: A python package for automated ranking of classifiers,” J. Open Source Softw. 5(48), 2173 (2020).CrossRefGoogle Scholar
Chen, J., Kao, S.-h., He, H., Zhuo, W., Wen, S., Lee, C.-H. and Chan, S.-H. G.. Run, Don’t Walk: Chasing Higher Flops for Faster Neural Networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023) pp. 12021–12031.Google Scholar
Yamanaka, E., Horiuchi, Y. and Nojima, I., “Emg-emg coherence during voluntary control of human standing tasks: A systematic scoping review,” Front. Neurosci.-SWITZ 17, 1145751 (2023).CrossRefGoogle ScholarPubMed
Liu, J., Sheng, Y. and Liu, H., “Corticomuscular coherence and its applications: A review,” Front. Hum. Neurosci. 13, 100 (2019).CrossRefGoogle ScholarPubMed
Gwin, J. T. and Ferris, D. P., “Beta-and gamma-range human lower limb corticomuscular coherence,” Front. Hum. Neurosci. 6, 258 (2012).CrossRefGoogle ScholarPubMed
Krasovsky, T., “Cognition, emotion, and movement in the context of rehabilitation,” Int. J. Environ. Res. Public Health 19(21), 14532 (2022).CrossRefGoogle ScholarPubMed
Lamers, S. M., Bolier, L., Westerhof, G. J., Smit, F. and Bohlmeijer, E. T., “The impact of emotional well-being on long-term recovery and survival in physical illness: A meta-analysis,” J. Behav. Med. 35(5), 538–547 (2012).CrossRefGoogle ScholarPubMed
Otaka, E., Osawa, A., Kato, K., Obayashi, Y., Uehara, S., Kamiya, M., Mizuno, K., Hashide, S. and Kondo, I., “Positive emotional responses to socially assistive robots in people with dementia: Pilot study,” JMIR Aging 7(1), e52443–e52443 (2024).CrossRefGoogle ScholarPubMed
Sadeghi, H., Allard, P. and Duhaime, M., “Contributions of lower-limb muscle power in gait of people without impairments,” Phys. Ther. 80(12), 1188–1196 (2000).CrossRefGoogle ScholarPubMed
Nazmi, N., Rahman, M. A. A., Yamamoto, S.-I., Ahmad, S. A., Zamzuri, H. and Mazlan, S. A., “A review of classification techniques of emg signals during isotonic and isometric contractions,” Sensors 16(8), 1304 (2016).CrossRefGoogle ScholarPubMed
Jeon, W., Jensen, J. L. and Griffin, L., “Muscle activity and balance control during sit-to-stand across symmetric and asymmetric initial foot positions in healthy adults,” Gait. Posture 71, 138–144 (2019).CrossRefGoogle ScholarPubMed
Eom, R.-i. and Lee, Y., “Comfort evaluation by wearing a gait-assistive rehabilitation robot,” J. Korean Soc. Clothing Textiles 44(6), 1107–1119 (2020).CrossRefGoogle Scholar
Wang, A., Hu, N., Yu, J., Liao, W., Zhang, J., Wu, X. and Pei, C.. Research on Robot Control System of Lower Limb Rehabilitation Robot Based on Human Gait comfort. In: 2019 International Conference on Advanced Mechatronic Systems (ICAMechS) (IEEE, 2019) pp. 34–39.CrossRefGoogle Scholar