Motor imagery mental practice: A potential role in Neurologic Rehabilitation (NR)

Ved Vrat Verma; Subbulakshmi Ganesan; Suvendu Narayan Mishra; Praney Madan; Abhishek Singla; Jaswant Goyal

doi:10.31893/multirev.2025ss0215

Keywords:

neuroplasticity

cognitive intervention

patient motivation

neural activation

Abstract

Motor imagery technique is used to improve motor learning and support neurological rehabilitation in stroke patients. Neurologic disorders like stroke, brain injury, and neurodegenerative conditions cause motor dysfunctions like physical disability and distressed feelings due to the impairments. Motor imagery mental practice (MIMP) is a nonpharmacological and cognitive intervention recognized to stimulate motor-related brain capacities without any motor movement. This systematic review explores how MIMP contributes to neurologic rehabilitation by improving motor function, neural plasticity, and patient engagement. The study synthesizes empirical and conceptual literature produced between 2020 and 2024 by thoroughly examining peer-reviewed publications and clinical reports from key academic databases like PubMed, Scopus, IEEE, and Web of Science. MIMP helps to promote motor recovery, patient motivation and the benefits of physiotherapy. This research addresses the structure of the intervention and the mechanisms of the neural response, and obstacles faced in integrating the clinical aspect. MIMP technique was a cost-effective alternative to conventional care since neurologic impairment is closely linked to decreased quality of life and long-term disability. Functional outcomes were enhanced with the use of structured imagery sessions, guided visualization, and motor rehearsal, which are exercised through motor pathways. Motor imagery is employed throughout the healing process, enabling patients to resume exercise even in cases of flaccid paralysis. The impact of motor imagery therapy on strokes is used to evaluate more randomized clinical studies. This review describes ideal therapies, evaluation models, and methods of incorporating MIMP into standard neurologic rehabilitation. These findings offer motor imagery that is used to supplement more conventional rehabilitation methods to improve functional outcomes and quality of life for stroke patients when paired with other therapeutic strategies.
References
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Verma, V. V., Ganesan, S., Mishra, S. N., Madan, P., Singla, A., & Goyal, J. (2025). Motor imagery mental practice: A potential role in Neurologic Rehabilitation (NR). Multidisciplinary Reviews, 8, 2025ss0215. https://doi.org/10.31893/multirev.2025ss0215

[1] Abraham, A., Duncan, R. P., & Earhart, G. M. (2021). The role of mental imagery in Parkinson’s disease rehabilitation. Brain Sciences, 11(2), 185. https://doi.org/10.3390/brainsci11020185

[2] Attallah, O., Abougharbia, J., Tamazin, M., & Nasser, A. A. (2020). A BCI system based on motor imagery for assisting people with motor deficiencies in the limbs. Brain Sciences, 10(11), 864. https://doi.org/10.3390/brainsci10110864

[3] Behrendt, F., Zumbrunnen, V., Brem, L., Suica, Z., Gäumann, S., Ziller, C., & Schuster-Amft, C. (2021). Effect of motor imagery training on motor learning in children and adolescents: A systematic review and meta-analysis. International Journal of Environmental Research and Public Health, 18(18), 9467. https://doi.org/10.3390/ijerph18189467

[4] Benzy, V. K., Vinod, A. P., Subasree, R., Alladi, S., & Raghavendra, K. (2020). Motor imagery hand movement direction decoding using brain computer interface to aid stroke recovery and rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(12), 3051–3062. https://doi.org/10.1109/TNSRE.2020.3039331

[5] Chaisaen, R., Autthasan, P., Mingchinda, N., Leelaarporn, P., Kunaseth, N., Tammajarung, S., & Wilaiprasitporn, T. (2020). Decoding EEG rhythms during action observation, motor imagery, and execution for standing and sitting. IEEE Sensors Journal, 20(22), 13776–13786. https://doi.org/10.1109/JSEN.2020.3005968

[6] Chen, J., Yu, Z., Gu, Z., & Li, Y. (2020). Deep temporal-spatial feature learning for motor imagery-based brain–computer interfaces. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(11), 2356–2366. https://doi.org/10.1109/TNSRE.2020.3023417

[7] Choi, J. W., Kim, B. H., Huh, S., & Jo, S. (2020). Observing actions through immersive virtual reality enhances motor imagery training. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(7), 1614–1622. https://doi.org/10.1109/TNSRE.2020.2998123

[8] Donati, D., Boccolari, P., Giorgi, F., Berti, L., Platano, D., & Tedeschi, R. (2024). Breaking the cycle of pain: The role of graded motor imagery and mirror therapy in complex regional pain syndrome. Biomedicines, 12(9), 2140. https://doi.org/10.3390/biomedicines12092140

[9] Gaur, P., Chowdhury, A., McCreadie, K., Pachori, R. B., & Wang, H. (2021). Logistic regression with tangent space-based cross-subject learning for enhancing motor imagery classification. IEEE Transactions on Cognitive and Developmental Systems, 14(3), 1188–1197. https://doi.org/10.1109/TCDS.2021.3099988

[10] Hussain, I., & Park, S. J. (2021). Quantitative evaluation of task-induced neurological outcome after stroke. Brain Sciences, 11(7), 900. https://doi.org/10.3390/brainsci11070900

[11] Jin, J., Liu, C., Daly, I., Miao, Y., Li, S., Wang, X., & Cichocki, A. (2020). Bispectrum-based channel selection for motor imagery based brain-computer interfacing. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(10), 2153–2163. https://doi.org/10.1109/TNSRE.2020.3020975

[12] Kanthack, T. F. D., Guillot, A., Clémençon, M., Debarnot, U., & Di Rienzo, F. (2020). Effect of physical fatigue elicited by continuous and intermittent exercise on motor imagery ability. Research Quarterly for Exercise and Sport, 91(3), 525–538. https://doi.org/10.1080/02701367.2019.1691709

[13] Lambert, K. J., Singhal, A., & Leung, A. W. (2022). Neurophysiological evidence of motor imagery training in Parkinson’s disease: A case series study. Brain Impairment, 23(2), 196–205. https://doi.org/10.1017/BrImp.2021.8

[14] Li, F., He, F., Wang, F., Zhang, D., Xia, Y., & Li, X. (2020). A novel simplified convolutional neural network classification algorithm of motor imagery EEG signals based on deep learning. Applied Sciences, 10(5), 1605. https://doi.org/10.3390/app10051605

[15] Mallik, A. K., Pandey, S. K., Srivastava, A., Kumar, S., & Kumar, A. (2020). Comparison of relative benefits of mirror therapy and mental imagery in phantom limb pain in amputee patients at a tertiary care center. Archives of Rehabilitation Research and Clinical Translation, 2(4), 100081. https://doi.org/10.1016/j.arrct.2020.100081

[16] Mane, R., Chouhan, T., & Guan, C. (2020). BCI for stroke rehabilitation: Motor and beyond. Journal of Neural Engineering, 17(4), 041001. https://doi.org/10.1088/1741-2552/aba162

[17] Morone, G., Ghanbari Ghooshchy, S., Pulcini, C., Spangu, E., Zoccolotti, P., Martelli, M., & Iosa, M. (2022). Motor imagery and sport performance: A systematic review on the PETTLEP model. Applied Sciences, 12(19), 9753. https://doi.org/10.3390/app12199753

[18] Paolucci, T., Cardarola, A., Colonnelli, P., Ferracuti, G., Gonnella, R., Murgia, M., & Mangone, M. (2020). Give me a kiss! An integrative rehabilitative training program with motor imagery and mirror therapy for recovery of facial palsy. European Journal of Physical and Rehabilitation Medicine, 56(1), 58–67. https://doi.org/10.23736/S1973-9087.19.05757-5

[19] Riquelme-Ros, J. V., Rodríguez-Bermúdez, G., Rodríguez-Rodríguez, I., Rodríguez, J. V., & Molina-García-Pardo, J. M. (2020). On the better performance of pianists with motor imagery-based brain-computer interface systems. Sensors, 20(16), 4452. https://doi.org/10.3390/s20164452

[20] Souto, D. O., Cruz, T. K. F., Coutinho, K., Julio-Costa, A., Fontes, P. L. B., & Haase, V. G. (2020). Effect of motor imagery combined with physical practice on upper limb rehabilitation in children with hemiplegic cerebral palsy. NeuroRehabilitation, 46(1), 53–63. https://doi.org/10.3233/NRE-192931

[21] Srimadumathi, V., & Reddy, M. R. (2024). Classification of motor imagery EEG signals using high resolution time-frequency representations and convolutional neural network. Biomedical Physics & Engineering Express, 10(3), 035025. https://doi.org/10.1088/2057-1976/ad3647

[22] Tarantino, V., Burgio, F., Toffano, R., Rigon, E., Meneghello, F., Weis, L., & Vallesi, A. (2021). Efficacy of a training on executive functions in potentiating rehabilitation effects in stroke patients. Brain Sciences, 11(8), 1002. https://doi.org/10.3390/brainsci11081002

[23] Villa-Berges, E., Laborda Soriano, A. A., Lucha-López, O., Tricas-Moreno, J. M., Hernández-Secorún, M., Gómez-Martínez, M., & Hidalgo-García, C. (2023). Motor imagery and mental practice in the subacute and chronic phases in upper limb rehabilitation after stroke: A systematic review. Occupational Therapy International, 2023(1), 3752889. https://doi.org/10.1155/2023/3752889

[24] Xuefang, L., Guihua, W., & Fengru, M. (2021). The effect of early cognitive training and rehabilitation for patients with cognitive dysfunction in stroke. International Journal of Methods in Psychiatric Research, 30(3), e1882. https://doi.org/10.1002/mpr.1882

[25] Zhang, R., Li, F., Zhang, T., Yao, D., & Xu, P. (2020). Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions. Brain Science Advances, 6(3), 224–241. https://doi.org/10.26599/BSA.2020.9050021

Abstract

References

How to cite