EEG-controlled functional electrical stimulation rehabilitation for chronic stroke: system design and clinical application

Abstract

Stroke is one of the most serious diseases that threaten human life and health. It is a major cause of death and disability in the clinic. New strategies for motor rehabilitation after stroke are undergoing exploration. We aimed to develop a novel artificial neural rehabilitation system, which integrates brain--computer interface (BCI) and functional electrical stimulation (FES) technologies, for limb motor function recovery after stroke. We conducted clinical trials (including controlled trials) in 32 patients with chronic stroke. Patients were randomly divided into the BCI-FES group and the neuromuscular electrical stimulation (NMES) group. The changes in outcome measures during intervention were compared between groups, and the trends of ERD values based on EEG were analyzed for BCI-FES group. Results showed that the increase in Fugl Meyer Assessment of the Upper Extremity (FMA-UE) and Kendall Manual Muscle Testing (Kendall MMT) scores of the BCI-FES group was significantly higher than that in the sham group, which indicated the practicality and superiority of the BCI-FES system in clinical practice. The change in the laterality coefficient (LC) values based on μ-ERD (ΔLC_m-ERD) had high significant positive correlation with the change in FMA-UE(r= 0.6093, P=0.012), which provides theoretical basis for exploring novel objective evaluation methods.

Keywords

brain–computer interface / functional electrical stimulation / electroencephalogram / laterality coefficient / chronic stroke

References

[1] Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Das SR, Delling FN, Djousse L, Elkind MSV, Ferguson JF, Fornage M, Jordan LC, Khan SS, Kissela BM, Knutson KL, Kwan TW, Lackland DT, Lewis TT, Lichtman JH, Longenecker CT, Loop MS, Lutsey PL, Martin SS, Matsushita K, Moran AE, Mussolino ME, O’Flaherty M, Pandey A, Perak AM, Rosamond WD, Roth GA, Sampson UKA, Satou GM, Schroeder EB, Shah SH, Spartano NL, Stokes A, Tirschwell DL, Tsao CW, Turakhia MP, VanWagner LB, Wilkins JT, Wong SS, Virani SS. Heart disease and stroke statistics—2019 update: a report from the American Heart Association. Circulation 2019; 139(10): e56–e528

[2] Yang Q, Tong X, Schieb L, Vaughan A, Gillespie C, Wiltz JL, King SC, Odom E, Merritt R, Hong Y, George MG. Vital signs: recent trends in stroke death rates—United States, 2000–2015. MMWR Morb Mortal Wkly Rep 2017; 66(35): 933–939

[3] Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, de Ferranti SD, Floyd J, Fornage M, Gillespie C, Isasi CR, Jiménez MC, Jordan LC, Judd SE, Lackland D, Lichtman JH, Lisabeth L, Liu S, Longenecker CT, Mackey RH, Matsushita K, Mozaffarian D, Mussolino ME, Nasir K, Neumar RW, Palaniappan L, Pandey DK, Thiagarajan RR, Reeves MJ, Ritchey M, Rodriguez CJ, Roth GA, Rosamond WD, Sasson C, Towfighi A, Tsao CW, Turner MB, Virani SS, Voeks JH, Willey JZ, Wilkins JT, Wu JH, Alger HM, Wong SS, Muntner P. Heart disease and stroke statistics—2017 update: a report from the American Heart Association. Circulation 2017; 135(10): e146–e603

[4] Nudo RJ, Wise BM, SiFuentes F, Milliken GW. Neural substrates for the effects of rehabilitative training on motor recovery after ischemic infarct. Science 1996; 272(5269): 1791–1794

[5] Taub E, Uswatte G, Elbert T. New treatments in neurorehabili-tation founded on basic research. Nat Rev Neurosci 2002; 3(3): 228–236

[6] Dimyan MA, Cohen LG. Neuroplasticity in the context of motor rehabilitation after stroke. Nat Rev Neurol 2011; 7(2): 76–85

[7] Nelson ME, Rejeski WJ, Blair SN, Duncan PW, Judge JO, King AC, Macera CA, Castaneda-Sceppa C. Physical activity and public health in older adults: recommendation from the American College of Sports Medicine and the American Heart Association. Med Sci Sports Exerc 2007; 39(8): 1435–1445

[8] Takeshima N, Rogers NL, Rogers ME, Islam MM, Koizumi D, Lee S. Functional fitness gain varies in older adults depending on exercise mode. Med Sci Sports Exerc 2007; 39(11): 2036–2043

[9] Yu W, An C, Kang H. Effects of resistance exercise using Thera-band on balance of elderly adults: a randomized controlled trial. J Phys Ther Sci 2013; 25(11): 1471–1473

[10] Takahashi T, Koizumi D, Islam MM, Watanabe M, Narita M, Takeshima N. Effects of passive exercise machine-based training on day care service user frail elderly. Rigakuryoho Kagaku 2011; 26: 209–213

[11] Takahashi T, Islam MM, Koizumi D, Narita M, Takeshima N. The effects of low intensity exercises performed by community-dwelling chronic stroke patients on passive movement-type machines. Rigakuryoho Kagaku 2012; 27(5): 545–551

[12] Rushton DN. Functional electrical stimulation and rehabilitation—an hypothesis. Med Eng Phys 2003; 25(1): 75–78

[13] Granat MH, Ferguson AC, Andrews BJ, Delargy M. The role of functional electrical stimulation in the rehabilitation of patients with incomplete spinal cord injury—observed benefits during gait studies. Paraplegia 1993; 31(4): 207–215

[14] Wassermann EM. Changes in motor representation with recovery of motor function after stroke: combined electrophysiological and imaging studies. EEG Clin Neurophysiol 1995; 97(4): S26

[15] Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006; 442(7099): 164–171

[16] Silvoni S, Ramos-Murguialday A, Cavinato M, Volpato C, Cisotto G, Turolla A, Piccione F, Birbaumer N. Brain-computer interface in stroke: a review of progress. Clin EEG Neurosci 2011; 42(4): 245–252

[17] Cervera MA, Soekadar SR, Ushiba J, Millán JDR, Liu M, Birbaumer N, Garipelli G. Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Ann Clin Transl Neurol 2018; 5(5): 651–663

[18] Qiu S, Yi W, Xu J, Qi H, Du J, Wang C, He F, Ming D. Event-related β EEG changes during active, passive movement and functional electrical stimulation of the lower limb. IEEE Trans Neural Syst Rehabil Eng 2016; 24(2): 283–290

[19] Young BM, Williams J, Prabhakaran V. BCI-FES: could a new rehabilitation device hold fresh promise for stroke patients? Expert Rev Med Devices 2014; 11(6): 537–539

[20] Stinear CM. Prediction of motor recovery after stroke: advances in biomarkers. Lancet Neurol 2017; 16(10): 826–836

[21] Looned R, Webb J, Xiao ZG, Menon C. Assisting drinking with an affordable BCI-controlled wearable robot and electrical stimulation: a preliminary investigation. J Neuroeng Rehabil 2014; 11(1): 51

[22] Biasiucci A, Leeb R, Iturrate I, Perdikis S, Al-Khodairy A, Corbet T, Schnider A, Schmidlin T, Zhang H, Bassolino M, Viceic D, Vuadens P, Guggisberg AG, Millán JDR. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nat Commun 2018; 9(1): 2421

[23] Do AH, Wang PT, King CE, Abiri A, Nenadic Z. Brain–computer interface controlled functional electrical stimulation system for ankle movement. J Neuroeng Rehabil 2011; 8(1): 49

[24] McCrimmon CM, King CE, Wang PT, Cramer SC, Nenadic Z, Do AH. Brain-controlled functional electrical stimulation therapy for gait rehabilitation after stroke: a safety study. J Neuroeng Rehabil 2015; 12(1): 57

[25] Zhang X, Elnady AM, Randhawa BK, Boyd LA, Menon C. Combining mental training and physical training with goal-oriented protocols in stroke rehabilitation: a feasibility case study. Front Hum Neurosci 2018; 12: 125

[26] Kim T, Kim S, Lee B. Effects of action observational training plus brain-computer interface-based functional electrical stimulation on paretic arm motor recovery in patient with stroke: a randomized controlled trial. Occup Ther Int 2016; 23(1): 39–47

[27] Chung E, Park SI, Jang YY, Lee BH. Effects of brain–computer interface-based functional electrical stimulation on balance and gait function in patients with stroke: preliminary results. J Phys Ther Sci 2015; 27(2): 513–516

[28] Chung E, Kim JH, Park DS, Lee BH. Effects of brain-computer interface-based functional electrical stimulation on brain activation in stroke patients: a pilot randomized controlled trial. J Phys Ther Sci 2015; 27(3): 559–562

[29] Jang YY, Kim TH, Lee BH. Effects of brain–computer interface-controlled functional electrical stimulation training on shoulder subluxation for patients with stroke: a randomized controlled trial. Occup Ther Int 2016; 23(2): 175–185

[30] Abduallatif NA, Elsherbini SG, Boshra BS, Yassine IA. Brain–computer interface controlled functional electrical stimulation system for paralyzed arm. 2016 8th Cairo International Biomedical Engineering Conference (CIBEC), Cairo, 2016. 48–51

[31] Bockbrader M, Annetta N, Friedenberg D, Schwemmer M, Skomrock N, Colachis S 4th, Zhang M, Bouton C, Rezai A, Sharma G, Mysiw WJ. Clinically significant gains in skillful grasp coordination by an individual with tetraplegia using an implanted brain–computer interface with forearm transcutaneous muscle stimulation. Arch Phys Med Rehabil 2019; 100(7): 1201–1217

[32] Likitlersuang J, Koh R, Gong X, Jovanovic L, Bolivar-Tellería I, Myers M, Zariffa J, Márquez-Chin C. EEG-controlled functional electrical stimulation therapy with automated grasp selection: a proof-of-concept study. Top Spinal Cord Inj Rehabil 2018; 24(3): 265–274

[33] Osuagwu BC, Wallace L, Fraser M, Vuckovic A. Rehabilitation of hand in subacute tetraplegic patients based on brain computer interface and functional electrical stimulation: a randomised pilot study. J Neural Eng 2016; 13(6): 065002

[34] Colachis SC 4th, Bockbrader MA, Zhang M, Friedenberg DA, Annetta NV, Schwemmer MA, Skomrock ND, Mysiw WJ, Rezai AR, Bresler HS, Sharma G. Dexterous control of seven functional hand movements using cortically-controlled transcutaneous muscle stimulation in a person with tetraplegia. Front Neurosci 2018; 12: 208

[35] Pfurtscheller G, Müller GR, Pfurtscheller J, Gerner HJ, Rupp R. ‘Thought’—control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci Lett 2003; 351(1): 33–36

[36] Ramoser H, Müller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 2000; 8(4): 441–446

[37] Makeig S. Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalogr Clin Neurophysiol 1993; 86(4): 283–293

[38] Lioi G, Fleury M, Butet S, Lécuyer A, Barillot C, Bonan I. Bimodal EEG-fMRI neurofeedback for stroke rehabilitation: a case report. Ann Phys Rehabil Med 2018; 61: e482–e483

[39] Wang T, Mantini D, Gillebert CR. The potential of real-time fMRI neurofeedback for stroke rehabilitation: a systematic review. Cortex 2018; 107: 148–165

[40] Perronnet L, Lécuyer A, Mano M, Bannier E, Lotte F, Clerc M, Barillot C. Unimodal versus bimodal EEG-fMRI neurofeedback of a motor imagery task. Front Hum Neurosci 2017; 11: 193

[41] Savelov AA, Shtark MB, Mel’nikov ME, Kozlova LI, Bezmaternykh DD, Verevkin EG, Petrovskii ED, Pokrovskii MA, Tsirkin GM, Rudych PD. Dynamics of fMRI and EEG parameters in a stroke patient assessed during a neurofeedback course focused on Brodmann Area 4 (M1). Bull Exp Biol Med 2019; 166(3): 394–398

[42] Bönstrup M, Schulz R, Cheng B, Feldheim J, Thomalla G, Hummel F, Gerloff C. P108. The effect of task effort on recovery-related brain activity following motor stroke assessed with fMRI and EEG. Clin Neurophysiol 2015; 126(8): e102

[43] Pfurtscheller G, Neuper C, Andrew C, Edlinger G. Foot and hand area mu rhythms. Int J Psychophysiol 1997; 26(1–3): 121–135

[44] Hobson HM, Bishop DVM. Mu suppression—a good measure of the human mirror neuron system? Cortex 2016; 82: 290–310

[45] Perry A, Stein L, Bentin S. Motor and attentional mechanisms involved in social interaction—evidence from mu and alpha EEG suppression. Neuroimage 2011; 58(3): 895–904

[46] Grefkes C, Fink GR. Connectivity-based approaches in stroke and recovery of function. Lancet Neurol 2014; 13(2): 206–216

[47] Ray AM, Figueiredo TDC, López-Larraz E, Birbaumer N, Ramos-Murguialday A. Brain oscillatory activity as a biomarker of motor recovery in chronic stroke. Hum Brain Mapp 2020; 41(5): 1296–1308

[48] Bushnell C, Bettger JP, Cockroft KM, Cramer SC, Edelen MO, Hanley D, Katzan IL, Mattke S, Nilsen DM, Piquado T, Skidmore ER, Wing K, Yenokyan G. Chronic stroke outcome measures for motor function intervention trials: expert panel recommendations. Circ Cardiovasc Qual Outcomes 2015; 8(6 Suppl 3): S163–S169

[49] Pandian S, Arya KN. Stroke-related motor outcome measures: do they quantify the neurophysiological aspects of upper extremity recovery? J Bodyw Mov Ther 2014; 18(3): 412–423

[50] Boord P, Barriskill A, Craig A, Nguyen H. Brain–computer interface-FES integration: towards a hands-free neuroprosthesis command system. Neuromodulation 2004; 7(4): 267–276

Acknowledgements

This work was supported by the National Key Research and Development Program of China (No. 2017YFB1300302), National Natural Science Foundation of China (Nos. 81630051, 91648122, and 81601565), and Tianjin Key Technology R&D Program (Nos. 17ZXRGGX00020 and 16ZXHLSY00270).

Compliance with ethics guidelines

Long Chen, Bin Gu, Zhongpeng Wang, Lei Zhang, Minpeng Xu, Shuang Liu, Feng He, and Dong Ming declare that they have no conflict of interest. All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000 (5). Informed consent was obtained from all patients included in the study.

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