17
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Toward methodologies for motor imagery enhancement: a tDCS-BCI study

ORCID Icon, , , , , & show all
Received 20 Jun 2023, Accepted 21 Jun 2024, Published online: 03 Jul 2024

References

  • Mulder T. Motor imagery and action observation: cognitive tools for rehabilitation. J Neural Transm. 2007;114(10):1265–1278. doi: 10.1007/s00702-007-0763-z
  • Hétu S, Grégoire M, Saimpont A, et al. The neural network of motor imagery: an ALE meta-analysis. Neurosci Biobehav Rev. 2013 Jun;37(5):930–949. doi: 10.1016/j.neubiorev.2013.03.017. Epub 2013 Apr 10. PMID: 23583615.
  • Lebon F, Horn U, Domin M, et al. Motor imagery training: Kinesthetic imagery strategy and inferior parietal fMRI activation. Hum Brain Mapp. 2018;39(4):1805–1813. doi: 10.1002/hbm.23956
  • Gentili R, Papaxanthis C, Pozzo T. Improvement and generalization of arm motor performance through motor imagery practice. Neuroscience. 2006;137(3):761–772. doi: 10.1016/j.neuroscience.2005.10.013
  • Mulder T, Zijlstra S, Zijlstra W, et al. The role of motor imagery in learning a totally novel movement. Exp Brain Res. 2004;154(2):211–217. doi: 10.1007/s00221-003-1647-6
  • García Carrasco D, Aboitiz Cantalapiedra J. Effectiveness of motor imagery or mental practice in functional recovery after stroke: a systematic review. Neurología (Engl Ed). 2016;31(1):43–52. doi: 10.1016/j.nrleng.2013.02.008
  • Mane R, Chouhan T, Guan C. BCI for stroke rehabilitation: motor and beyond. J Neural Eng. 2020;17(4):041001. doi: 10.1088/1741-2552/aba162
  • Pichiorri F, Morone G, Petti M, et al. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol. 2015;77(5):851–865. doi: 10.1002/ana.24390
  • Garrison KA, Winstein CJ, Aziz-Zadeh L. The mirror neuron system: a neural substrate for methods in stroke rehabilitation. Neurorehabil Neural Repair. 2010;24(5):404–412. doi: 10.1177/1545968309354536
  • Reynolds JE, Thornton AL, Elliott C, et al. A systematic review of mirror neuron system function in developmental coordination disorder: imitation, motor imagery, and neuroimaging evidence. In: Research in Developmental Disabilities. Vol. 47. Elsevier Inc; 2015. p. 234–283. doi: 10.1016/j.ridd.2015.09.015
  • Höhne J, Holz E, Staiger-Sälzer P, et al. Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution. PLOS ONE. 2014;9(8):e104854. doi: 10.1371/journal.pone.0104854
  • Dobkin BH. Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol. 2007;579(3):637–642. doi: 10.1113/jphysiol.2006.123067
  • Kübler A, Birbaumer N. Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients?. Clin Neurophysiol. 2008;119(11):2658–2666. doi: 10.1016/j.clinph.2008.06.019
  • Nijboer F, Sellers EW, Mellinger J, et al. A P300-based brain-computer interface for people with amyotrophic lateral sclerosis. Clin Neurophysiol. 2008;119(8):1909–1916. doi: 10.1016/j.clinph.2008.03.034
  • Khan MA, Das R, Iversen HK, et al. Review on motor imagery based BCI systems for upper limb post-stroke neurorehabilitation: from designing to application. In: Computers in Biology and Medicine. Vol. 123. Elsevier Ltd; 2020. doi: 10.1016/j.compbiomed.2020.103843
  • Wolpaw JR, Birbaumer N, Mcfarland DJ, et al. Brain–computer interfaces for communication and control. Clin Neurophysiol. 2002;113(6):767–791. doi: 10.1016/S1388-2457(02)00057-3
  • Wolpaw JR, Mcfarland DJ, Neat GW, et al. An EEG-based brain-computer interface for cursor control. Electroencephalography Clin Neurophysiol. 1991;78(3):252–259. doi: 10.1016/0013-4694(91)90040-B
  • Renard Y, Lotte F, Gibert G, et al. OpenViBE: an open-source software platform to design, test, and use brain–computer interfaces in real and virtual environments Presence: Teleoper Virt Environ. 2010;1(1):35–53. doi: 10.1162/pres.19.1.35
  • Ahn M, Jun SC. Performance variation in motor imagery brain-computer interface: a brief review. J Neurosci Methods. 2015;243:103–110. doi: 10.1016/j.jneumeth.2015.01.033
  • Edlinger G, Allison BZ, Guger C. How many people can use a BCI system? In: Kansaku K, Cohen LG Birbaumer N, editors. Clinical Systems Neuroscience. Springer Japan; 2015. p. 33–66. doi: 10.1007/978-4-431-55037-2_3
  • Lotte F, Bougrain L, Cichocki A, et al. A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J Neural Eng. 2018;15(3). Institute of Physics Publishing. 031005. doi: 10.1088/1741-2552/aab2f2
  • Blanco-Mora DA, Aldridge A, Jorge C, et al. Finding the optimal time window for increased classification accuracy during motor imagery. In: BIODEVICES 2021 - 14th International Conference on Biomedical Electronics and Devices; Part of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2021; 2021. p. 144–151. doi: 10.5220/0010316101440151
  • Blanco-Mora DA, Aldridge A, Jorge C, et al. Impact of age, VR, immersion, and spatial resolution on classifier performance for a MI-based BCI. Brain-Comput Interface. 2022;9(3):169–178. doi: 10.1080/2326263X.2022.2054606
  • Ahn M, Cho H, Ahn S, et al. High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery. PLOS ONE. 2013;8(11): e80886. doi: 10.1371/journal.pone.0080886
  • Keil A, Bernat EM, Cohen MX, et al. Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series. Psychophysiology. 2022;59(5). doi: 10.1111/psyp.14052
  • Cohen MX. Analyzing neural time series data: theory and practice. 2014.
  • Jacobson L, Koslowsky M, Lavidor M. TDCS polarity effects in motor and cognitive domains: a meta-analytical review. Exp Brain Res. 2012;216(1):1–10. doi: 10.1007/s00221-011-2891-9
  • Nitsche MA, Paulus W. Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation. J Physiol. 2000;527(3):633–639. doi: 10.1111/j.1469-7793.2000.t01-1-00633.x
  • Ang KK, Guan C, Phua KS, et al. Facilitating effects of transcranial direct current stimulation on motor imagery brain-computer interface with robotic feedback for stroke rehabilitation. Arch Phys Med Rehabil. 2015;96(3):S79–S87. doi: 10.1016/j.apmr.2014.08.008
  • van Hoornweder S, Vanderzande L, Bloemers E, et al. The effects of transcranial direct current stimulation on upper-limb function post-stroke: a meta-analysis of multiple-session studies. Clin Neurophysiol. 2021;132(8):1897–1918. doi: 10.1016/j.clinph.2021.05.015
  • Gerloff C, Andres FG. Bimanual coordination and interhemispheric interaction. Acta Psychol (Amst). 2002;110(2–3):161–186. www.elsevier.com/locate/actpsy doi: 10.1016/S0001-6918(02)00032-X
  • Maes C, Gooijers J, Orban de Xivry JJ, et al. Two hands, one brain, and aging. In: Neuroscience and Biobehavioral Reviews. Vol. 75. Elsevier Ltd; 2017. p. 234–256. doi: 10.1016/j.neubiorev.2017.01.052
  • Gooijers J, Swinnen SP. Interactions between brain structure and behavior: the corpus callosum and bimanual coordination. In: Neuroscience and Biobehavioral Reviews. Vol. 43. Elsevier Ltd; 2014. p. 1–19. doi: 10.1016/j.neubiorev.2014.03.008
  • King JT, John AR, Wang YK, et al. Brain connectivity changes during bimanual and rotated motor imagery. IEEE J Transl Eng Health Med. 2022;10:1–8. doi: 10.1109/JTEHM.2022.3167552
  • Saimpont A, Mercier C, Malouin F, et al. Anodal transcranial direct current stimulation enhances the effects of motor imagery training in a finger tapping task. Eur J Neurosci. 2016;43(1):113–119. doi: 10.1111/ejn.13122
  • Saruco E, Rienzo FD, Nunez-Nagy S, et al. Anodal tDCS over the primary motor cortex improves motor imagery benefits on postural control: a pilot study. Sci Rep. 2017;7(1). doi: 10.1038/s41598-017-00509-w
  • Lapenta OM, Minati L, Fregni F, et al. Jepense donc je fais: transcranial direct current stimulation modulates brain oscillations associated with motor imagery and movement observation. Front Human Neurosci. 2013;7. doi: 10.3389/fnhum.2013.00256
  • Matsumoto J, Fujiwara T, Takahashi O, et al. Modulation of mu rhythm desynchronization during motor imagery by transcranial direct current stimulation. J Neuroeng Rehabil. 2010;7(1):27. http://www.jneuroengrehab.com/content/7/1/27
  • Wei P, He W, Zhou Y, et al. Performance of motor imagery brain-computer interface based on anodal transcranial direct current stimulation modulation. IEEE Trans Neural Syst Rehabil Eng. 2013;21(3):404–415. doi: 10.1109/TNSRE.2013.2249111
  • Van Hoornweder S, Blanco-Mora DA, Depestele S, et al. Age and interlimb coordination complexity modulate oscillatory spectral dynamics and large-scale functional connectivity. Neuroscience. 2022;496:1–15. doi: 10.1016/j.neuroscience.2022.06.008
  • Antal A, Alekseichuk I, Bikson M, et al. Low intensity transcranial electric stimulation: safety, ethical, legal regulatory and application guidelines. Clin Neurophysiol. 2017;128(9):1774–1809). Elsevier Ireland Ltd. doi: 10.1016/j.clinph.2017.06.001
  • Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsych. 1971 Mar;9(1): 97–113. doi: 10.1016/0028-3932(71)90067-4. PMID: 5146491.
  • Malouin F, Richards CL, Jackson PL, et al. The kinesthetic and visual imagery questionnaire (KVIQ) for assessing motor imagery in persons with physical disabilities: a reliability and construct validity study. J Neurol Phys Ther. 2007;31(1):20–29. doi: 10.1097/01.NPT.0000260567.24122.64
  • Ko M-H. Safety of transcranial direct current stimulation in neurorehabilitation. Brain Neurorehabil. 2021;14(1). doi: 10.12786/bn.2021.14.e9
  • Brooke J. SUS: a quick and dirty usability scale system usability scale. 1995. Available from: https://www.researchgate.net/publication/228593520
  • General Assembly of the World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. J Am Coll Dent. 2014;81(3): 14–8. PMID: 25951678.
  • Sisti HM, Geurts M, Clerckx R, et al. Testing multiple coordination constraints with a novel bimanual visuomotor task. PLOS ONE. 2011;6(8):e23619. doi: 10.1371/journal.pone.0023619
  • Verstraelen S, van Dun K, Duque J, et al. Induced suppression of the left dorsolateral prefrontal cortex favorably changes interhemispheric communication during bimanual coordination in older adults–a neuronavigated rTMS study. Front Aging Neurosci. 2020;12:12. doi: 10.3389/fnagi.2020.00149
  • Vourvopoulos A, Ferreira A, Badia SBI. NeuRow: an immersive VR environment for motor-imagery training with the use of brain-computer interfaces and vibrotactile feedback. In: PhyCS 2016 - Proceedings of the 3rd International Conference on Physiological Computing Systems; 2016. p. 43–53. doi: 10.5220/0005939400430053
  • Puonti O, van Leemput K, Saturnino GB, et al. Accurate and robust whole-head segmentation from magnetic resonance images for individualized head modeling. Neuroimage. 2020;219:219. doi: 10.1016/j.neuroimage.2020.117044
  • Thielscher A, Antunes A, Saturnino GB. Field modeling for transcranial magnetic stimulation: a useful tool to understand the physiological effects of TMS?. 2015. doi: 10.0/Linux-x86_64
  • Pfurtscheller G, Neuper C, Muüller GR, et al. Graz-BCI: state of the art and clinical applications. IEEE Trans Neural Syst Rehabil Eng. 2003;11(2):177–180. doi: 10.1109/TNSRE.2003.814454
  • Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9–21. doi: 10.1016/j.jneumeth.2003.10.009
  • Gustafsson FK. Determining the initial states in forward-backward filtering. In: IEEE Trans Signal Process. 1994;44:988–992.
  • Delorme A. Clean rawdata (2.0). 2021.
  • Pion-Tonachini L, Kreutz-Delgado K, Makeig S. ICLabel: an automated electroencephalographic independent component classifier, dataset, and website. Neuroimage. 2019;198:181–197. doi: 10.1016/j.neuroimage.2019.05.026
  • Perrin F, Pernier J, Bertrand O, et al. Spherical splines for scalp potential and current density mapping. Electroencephalogr Clin Neurophysiol. 1989;72(2):184–187. doi: 10.1016/0013-4694(89)90180-6
  • Van Hoornweder S, Blanco-Mora DA, Depestele S, et al. Aging and complexity effects on hemisphere-dependent movement-related beta desynchronization during bimanual motor planning and execution. Brain Sci. 2022;12(11):1444. doi: 10.3390/brainsci12111444
  • RStudio Team. RStudio: integrated Development for R. Boston, MA: RStudio, PBC; 2020. http://www.rstudio.com/
  • Bourisly AK, Shuaib A. Neurophysiological effects of aging: a P200 ERP study. Transl Neurosci. 2018;9(1):61–66. doi: 10.1515/tnsci-2018-0011
  • Cebolla AM, Petieau M, Cevallos C, et al. Long-lasting cortical reorganization as the result of motor imagery of throwing a ball in a virtual tennis court. Front Psychol. 2015;6(DEC). doi: 10.3389/fpsyg.2015.01869
  • Potts GF. An ERP index of task relevance evaluation of visual stimuli. Brain Cogn. 2004;56(1):5–13. doi: 10.1016/j.bandc.2004.03.006
  • Mcfarland DJ, Miner LA, Vaughan TM, et al. Mu and beta rhythm topographies during motor imagery and actual movements. Brain Topogr. 2000;12(3):177–186. doi: 10.1023/A:1023437823106
  • Neuper C, Pfurtscheller G. Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates. Int J Psychophysiol. 2001;43(1):41–58. doi: 10.1016/S0167-8760(01)00178-7
  • Neuper C, Scherer R, Wriessnegger S, et al. Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. Clin Neurophysiol. 2009;120(2):239–247. doi: 10.1016/j.clinph.2008.11.015
  • Abenna S, Nahid M, Bajit A. Motor imagery based brain-computer interface: improving the EEG classification using delta rhythm and LightGBM algorithm. Biomedical Signal Processing And Control. 2022;71:71. doi: 10.1016/j.bspc.2021.103102
  • Ahn M, Ahn S, Hong JH, et al. Gamma band activity associated with BCI performance: simultaneous MEG/EEG study. Front Human Neurosci. 2013:7(DEC. doi: 10.3389/fnhum.2013.00848
  • Marcuse LV, Fields MC, Yoo J (Jenna). The normal adult EEG. In: Rowan’s Primer of EEG. Elsevier; 2016. p. 39–66. doi: 10.1016/b978-0-323-35387-8.00002-0
  • Caulfield KA, George MS. Optimized APPS-tDCS electrode position, size, and distance doubles the on-target stimulation magnitude in 3000 electric field models. Sci Rep. 2022;12(1). doi: 10.1038/s41598-022-24618-3
  • Van Hoornweder S, Caulfield KA, Nitsche M, et al. Addressing transcranial electrical stimulation variability through prospective individualized dosing of electric field strength in 300 participants across two samples: the 2-SPED approach. J Neural Eng. 2022;19(5):056045. doi: 10.1088/1741-2552/ac9a78

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.