Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke

Takashi Ono, Keiichiro Shindo, Kimiko Kawashima, Naoki Ota, Mari Ito, Tetsuo Ota, Masahiko Mukaino, Toshiyuki Fujiwara, Akio Kimura, Meigen Liu, Junichi Ushiba, Takashi Ono, Keiichiro Shindo, Kimiko Kawashima, Naoki Ota, Mari Ito, Tetsuo Ota, Masahiko Mukaino, Toshiyuki Fujiwara, Akio Kimura, Meigen Liu, Junichi Ushiba

Abstract

Recent studies have shown that scalp electroencephalogram (EEG) based brain-computer interface (BCI) has a great potential for motor rehabilitation in stroke patients with severe hemiplegia. However, key elements in BCI architecture for functional recovery has yet to be clear. We in this study focused on the type of feedback to the patients, which is given contingently to their motor-related EEG in a BCI context. The efficacy of visual and somatosensory feedbacks was compared by a two-group study with the chronic stroke patients who are suffering with severe motor hemiplegia. Twelve patients were asked an attempt of finger opening in the affected side repeatedly, and the event-related desynchronization (ERD) in EEG of alpha and beta rhythms was monitored over bilateral parietal regions. Six patients were received a simple visual feedback in which the hand open/grasp picture on screen was animated at eye level, following significant ERD. Six patients were received a somatosensory feedback in which the motor-driven orthosis was triggered to extend the paralyzed fingers from 90 to 50°. All the participants received 1-h BCI treatment with 12-20 training days. After the training period, while no changes in clinical scores and electromyographic (EMG) activity were observed in visual feedback group after training, voluntary EMG activity was newly observed in the affected finger extensors in four cases and the clinical score of upper limb function in the affected side was also improved in three participants in somatosensory feedback group. Although the present study was conducted with a limited number of patients, these results imply that BCI training with somatosensory feedback could be more effective for rehabilitation than with visual feedback. This pilot trial positively encouraged further clinical BCI research using a controlled design.

Keywords: brain-computer interface rehabilitation; motor imagery; somatosensory feedback; visual feedback.

Figures

Figure 1
Figure 1
(A) Electrode position (B) Visual feedback paradigm (C) Somatosensory feedback paradigm cited from Shindo et al. (2011), partially revised.
Figure 2
Figure 2
ERD evaluation over both primary sensorimotor areas. White bars represent ERD values before training and black bars represent the ERD values after training. Numbers on x axis represent participant numbers.
Figure 3
Figure 3
BCI performance accuracy. White bars represent ERD values before training and black bars represent the ERD values after training. Numbers on x axis represent participant numbers.
Figure 4
Figure 4
Comparison of EMG activity before and after BCI. The horizontal bars represent the period during which participants opened their paralyzed hands. The data from participant 7 to participant 12 are from Shindo et al. (2011). Permission from Foundation for Rehabilitation Information.
Figure 5
Figure 5
Stroke Impairment Assessment Set (SIAS) finger function scores.

References

    1. Alkadhi H., Brugger P., Boendermaker S. H., Crelier G., Curt A., Hepp-Reymond M.-C., et al. (2005). What disconnection tells about motor imagery: evidence from paraplegic patients. Cereb. Cortex 15, 131–140 10.1093/cercor/bhh116
    1. Arya K. N., Pandian S., Verma R., Garg R. K. (2011). Movement therapy induced neural reorganization and motor recovery in stroke: a review. J. Bodyw. Mov. Ther. 15, 528–537 10.1016/j.jbmt.2011.01.023
    1. Broetz D., Braun C., Weber C., Soekadar S. R., Caria A., Birbaumer N. (2010). Combination of brain-computer interface training and goal-directed physical therapy in chronic stroke: a case report. Neurorehabil. Neural Repair 24, 674–679 10.1177/1545968310368683
    1. Buch E., Weber C., Cohen L. G., Braun C., Dimyan M. A., Ard T., et al. (2008). Think to move: a neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke 39, 910–917 10.1161/STROKEAHA.107.505313
    1. Caria A., Weber C., Brötz D., Ramos A., Ticini L. F., Gharabaghi A., et al. (2011). Chronic stroke recovery after combined BCI training and physiotherapy: a case report. Psychophysiology 48, 578–582 10.1111/j.1469-8986.2010.01117.x
    1. Carmichael S. T. (2006). Cellular and molecular mechanisms of neural repair after stroke: making waves. Ann. Neurol. 59, 735–742 10.1002/ana.20845
    1. Chino N., Sonoda S., Domen K., Saitoh E., Kimura A. (1994). Stroke Impairment Assessment Set (SIAS). A new evaluation instrument for stroke patients. Jpn. J. Rehabil. Med. 31, 119–125 10.2490/jjrm1963.31.119
    1. Cincotti F., Pichiorri F., Aricò P., Aloise F., Leotta F., de Vico Fallani F., et al. (2012). EEG-based brain-computer interface to support post-stroke motor rehabilitation of the upper limb. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2012, 4112–4115 10.1109/EMBC.2012.6346871
    1. Daly J. J., Cheng R., Rogers J., Litinas K., Hrovat K., Dohring M. (2009). Feasibility of a new application of noninvasive brain computer interface (BCI): a case study of training for recovery of volitional motor control after stroke. J. Neurol. Phys. Ther. 33, 203–211 10.1097/NPT.0b013e3181c1fc0b
    1. Daly J. J., Wolpaw J. R. (2008). Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 7, 1032–1043 10.1016/S1474-4422(08)70223-0
    1. Duncan P. W., Lai S. M., Keighley J. (2000). Defining post-stroke recovery: implications for design and interpretation of drug trials. Neuropharmacology 39, 835–841 10.1016/S0028-3908(00)00003-4
    1. Ertelt D., Small S., Solodkin A., Dettmers C., McNamara A., Binkofski F., et al. (2007). Action observation has a positive impact on rehabilitation of motor deficits after stroke. Neuroimage 36 Suppl. 2, T164–T173 10.1016/j.neuroimage.2007.03.043
    1. Garrison K. A., Winstein C. J., Aziz-Zadeh L. (2010). The mirror neuron system: a neural substrate for methods in stroke rehabilitation. Neurorehabil. Neural Repair 24, 404–412 10.1177/1545968309354536
    1. Grotta J. C., Noser E. A., Ro T., Boake C., Levin H., Aronowski J., et al. (2004). Constraint-induced movement therapy. Stroke J. Cereb. Circ. 35, 2699–2701 10.1161/01.STR.0000143320.64953.c4
    1. Hashimoto Y., Ushiba J., Kimura A., Liu M., Tomita Y. (2010). Change in brain activity through virtual reality-based brain-machine communication in a chronic tetraplegic subject with muscular dystrophy. BMC Neurosci. 11:117 10.1186/1471-2202-11-117
    1. Hwang H.-J., Kwon K., Im C.-H. (2009). Neurofeedback-based motor imagery training for brain-computer interface (BCI). J. Neurosci. Methods 179, 150–156 10.1016/j.jneumeth.2009.01.015
    1. Johnston M. V. (2004). Clinical disorders of brain plasticity. Brain Dev. 26, 73–80 10.1016/S0387-7604(03)00102-5
    1. Jørgensen H. S., Nakayama H., Raaschou H. O., Vive-Larsen J., Støier M., Olsen T. S. (1995). Outcome and time course of recovery in stroke. Part II: time course of recovery. The Copenhagen Stroke Study. Arch. Phys. Med. Rehabil. 76, 406–412
    1. Kimberley T. J., Lewis S. M., Auerbach E. J., Dorsey L. L., Lojovich J. M., Carey J. R. (2004). Electrical stimulation driving functional improvements and cortical changes in subjects with stroke. Exp. Brain Res. 154, 450–460 10.1007/s00221-003-1695-y
    1. Lin K.-C., Chung H.-Y., Wu C.-Y., Liu H.-L., Hsieh Y.-W., Chen I.-H., et al. (2010). Constraint-induced therapy versus control intervention in patients with stroke: a functional magnetic resonance imaging study. Am. J. Phys. Med. Rehabil. 89, 177–185 10.1097/PHM.0b013e3181cf1c78
    1. Lopes da Silva F. (1991). Neural mechanisms underlying brain waves: from neural membranes to networks. Electroencephalogr. Clin. Neurophysiol. 79, 81–93
    1. Mark V. W., Taub E., Morris D. M. (2006). Neuroplasticity and constraint-induced movement therapy. Eur. Medicophys. 42, 269–284
    1. Mukaino M., Ono T., Shindo K., Fujiwara T., Ota T., Kimura A., et al. (2014). Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke. J. Rehabil. Med. 46, 378–382 10.2340/16501977-1785
    1. Murphy T. H., Corbett D. (2009). Plasticity during stroke recovery: from synapse to behaviour. Nat. Rev. Neurosci. 10, 861–872 10.1038/nrn2735
    1. Nakayama H., Jørgensen H. S., Raaschou H. O., Olsen T. S. (1994). Recovery of upper extremity function in stroke patients: the Copenhagen Stroke Study. Arch. Phys. Med. Rehabil. 75, 394–398
    1. Neuper C., Scherer R., Wriessnegger S., Pfurtscheller G. (2009). Motor imagery and action observation: modulation of sensorimotor brain rhythms during mental control of a brain-computer interface. Clin. Neurophysiol. 120, 239–247 10.1016/j.clinph.2008.11.015
    1. Neuper C., Wörtz M., Pfurtscheller G. (2006). ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog. Brain Res. 159, 211–222 10.1016/S0079-6123(06)59014-4
    1. Nudo R. J. (2006). Mechanisms for recovery of motor function following cortical damage. Curr. Opin. Neurobiol. 16, 638–644 10.1016/j.conb.2006.10.004
    1. Peckham P. H., Mortimer J. T., Marsolais E. B. (1980). Controlled prehension and release in the C5 quadriplegic elicited by functional electrical stimulation of the paralyzed forearm musculature. Ann. Biomed. Eng. 8, 369–388
    1. Pfurtscheller G., Neuper C. (2001). Motor imagery and direct brain-computer communication. Proc. IEEE 89, 1123–1134 10.1109/5.939829
    1. Pfurtscheller G., Neuper C., Flotzinger D., Pregenzer M. (1997). EEG-based discrimination between imagination of right and left hand movement. Electroencephalogr. Clin. Neurophysiol. 103, 642–651
    1. Prasad G., Herman P., Coyle D., McDonough S., Crosbie J. (2009). Using motor imagery based brain-computer interface for post-stroke rehabilitation, in 4th International IEEE/EMBS Conference on Neural Engineering, 2009. NER'09 (Antalya: ), 258–262
    1. Ramos-Murguialday A., Broetz D., Rea M., Läer L., Yilmaz O., Brasil F. L., et al. (2013). Brain-machine-interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74, 100–108 10.1002/ana.23879
    1. Rozelle G. R., Budzynski T. H. (1995). Neurotherapy for stroke rehabilitation: a single case study. Biofeedback Self-Regul. 20, 211–228
    1. Shindo K., Kawashima K., Ushiba J., Ota N., Ito M., Ota T., et al. (2011). Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study. J. Rehabil. Med. 43, 951–957 10.2340/16501977-0859
    1. Steriade M., Llinás R. R. (1988). The functional states of the thalamus and the associated neuronal interplay. Physiol. Rev. 68, 649–742
    1. Takemi M., Masakado Y., Liu M., Ushiba J. (2013). Event-related desynchronization reflects down-regulation of intracortical inhibition in human primary motor cortex. J. Neurophysiol. 110, 1158–1166 10.1152/jn.01092.2012
    1. Taub E., Uswatte G., King D. K., Morris D., Crago J. E., Chatterjee A. (2006). A placebo-controlled trial of constraint-induced movement therapy for upper extremity after stroke. Stroke 37, 1045–1049 10.1161/01.STR.0000206463.66461.97

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