Using a brain-machine interface to control a hybrid upper limb exoskeleton during rehabilitation of patients with neurological conditions
Tóm tắt
As a consequence of the increase of cerebro-vascular accidents, the number of people suffering from motor disabilities is raising. Exoskeletons, Functional Electrical Stimulation (FES) devices and Brain-Machine Interfaces (BMIs) could be combined for rehabilitation purposes in order to improve therapy outcomes. In this work, a system based on a hybrid upper limb exoskeleton is used for neurological rehabilitation. Reaching movements are supported by the passive exoskeleton ArmeoSpring and FES. The movement execution is triggered by an EEG-based BMI. The BMI uses two different methods to interact with the exoskeleton from the user’s brain activity. The first method relies on motor imagery tasks classification, whilst the second one is based on movement intention detection. Three healthy users and five patients with neurological conditions participated in the experiments to verify the usability of the system. Using the BMI based on motor imagery, healthy volunteers obtained an average accuracy of 82.9 ± 14.5 %, and patients obtained an accuracy of 65.3 ± 9.0 %, with a low False Positives rate (FP) (19.2 ± 10.4 % and 15.0 ± 8.4 %, respectively). On the other hand, by using the BMI based on detecting the arm movement intention, the average accuracy was 76.7 ± 13.2 % for healthy users and 71.6 ± 15.8 % for patients, with 28.7 ± 19.9 % and 21.2 ± 13.3 % of FP rate (healthy users and patients, respectively). The accuracy of the results shows that the combined use of a hybrid upper limb exoskeleton and a BMI could be used for rehabilitation therapies. The advantage of this system is that the user is an active part of the rehabilitation procedure. The next step will be to verify what are the clinical benefits for the patients using this new rehabilitation procedure.
Tài liệu tham khảo
WHO. Global Health and Aging. Bethesda, Maryland: NIH Publication, U.S. Department of Health and Human Services; 2011.
American Heart Association. Heart Disease and Stroke Statistics-2012 Update: a Report from the American Heart Association. Greenville Avenue, Dallas: American Heart Association; 2012.
Heidenreich PA, Trogdon JG, Khavjou OA, Butler J, Dracup K, Ezekowitz MD, et al.Forecasting the future of cardiovascular disease in the united states: a policy statement from the american heart association. Circulation. 2011; 123:933–44.
World Health Organization. The World Health Report: Reducing Risks, Promoting Healthy Life. France: World Health Organization; 2002.
Schaechter JD. Motor rehabilitation and brain plasticity after hemiparetic stroke. Prog Neurobiol. 2004; 73(1):61–72.
Hendricks HT, van Limbeek J, Geurts AC, Zwarts MJ. Motor recovery after stroke: a systematic review of the literature. Arch Phys Med Rehabil. 2002; 83(11):1629–37.
Kwakkel G, Kollen BJ, Krebs HI. Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review. Neurorehabil Neural Repair. 2008; 22(2):111–21.
Prange GB, Jannink MJA, Groothuis-Oudshoorn CGM, Hermens HJ, Ijzerman MJ. Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. J Rehabil Res Dev. 2006; 43(2):171–84.
Cauraugh JH, Light K, Kim S, Thigpen M, Behrman A. Chronic motor dysfunction after stroke : Recovering wrist and finger extension by electromyography-triggered neuromuscular stimulation. Stroke. 2000; 31(6):1360–4.
Popovic MB, Popovic DB, Sinkjæ T, Stefanovic A, Schwirtlich L. Restitution of reaching and grasping promoted by functional electrical therapy. Artif Organs. 2002; 26(3):271–5.
Freeman C, Rogers E, Hughes A-M, Burridge JH, Meadmore K. Iterative learning control in health care: Electrical stimulation and robotic-assisted upper-limb stroke rehabilitation. IEEE Control Syst. 2012; 32(1):18–43.
Alon G, McBride K, Ring H. Improving selected hand functions using a noninvasive neuroprosthesis in persons with chronic stroke. J Stroke Cerebrovasc Dis: Official J National Stroke Assoc. 2002; 11(2):99–106.
Del-Ama AJ, Koutsou AD, Moreno JC, De-los-Reyes A, Gil-Agudo A, Pons JL. Review of hybrid exoskeletons to restore gait following spinal cord injury. J Reinf Plast Compos. 2012; 49(4):497.
Hughes AM, Freeman CT, Burridge JH, Chappell PH, Lewin PL, Rogers E. Feasibility of iterative learning control mediated by functional electrical stimulation for reaching after stroke. Neurorehabil Neural Repair. 2009.
Ridding M, Brouwer B, Miles T, Pitcher J, Thompson P. Changes in muscle responses to stimulation of the motor cortex induced by peripheral nerve stimulation in human subjects. Exp Brain Res. 2000; 131(1):135–43.
Sale P, Franceschini M, Mazzoleni S, Palma E, Agosti M, Posteraro F. Effects of upper limb robot-assisted therapy on motor recovery in subacute stroke patients. J Neuroeng Rehabil. 2014; 11(104):1–8.
Hötting K, Röder B. Beneficial effects of physical exercise on neuroplasticity and cognition. Neurosci Biobehav Rev. 2013; 37:2243–57.
Witkowski M, Cortese M, Cempini M, Mellinger J, Vitiello N, Soekadar SR. Enhancing brain-machine interface (bmi) control of a hand exoskeleton using electrooculography (eog). J Neuroeng Rehabil. 2014; 11(165):1–6.
Steinisch M, Tana MG, Comani S. A post-stroke rehabilitation system integrating robotics, vr and high-resolution eeg imaging. IEEE Trans Neural Syst Rehabil Eng. 2013; 21(5):849–59.
López-Larraz E, Montesano L, Gil-Agudo Á, Minguez J. Continuous decoding of movement intention of upper limb self-initiated analytic movements from pre-movement eeg correlates. J Neuroeng Rehabil. 2014; 11(153):1–15.
Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol. 2008; 7(11):1032–43.
Ferreira A, Bastos-Filho TF, Sarcinelli-Filho M, Martín JL, García JC, Mazo M. Improvements of a brain-computer interface applied to a robotic wheelchair. In: Biomedical Engineering Systems and Technologies - International Joint Conference BIOSTEC. Berlin, Heidelberg, Germany: Springer: 2009. p. 64–73.
Hortal E, Planelles D, Costa A, Iáñez E, Úbeda A, Azorín JM, Fernández E. Svm-based brain-machine interface for controlling a robot arm through four mental tasks. Neurocomputing. 2014.
Citi L, Poli R, Cinel C, Sepulveda F. P300-based bci mouse with genetically-optimized analogue control. IEEE Trans Neural Syst Rehabil Eng. 2008; 16(1):51–61.
Sirvent JL, IáÑez E, Úbeda A, Azorín JM. Visual evoked potential-based brain-machine interface applications to assist disabled people. Expert Syst Appl. 2012; 39(9):7908–18.
Ramos-Murguialday A, Broetz D, Rea M, Läer L, Yilmaz O, Brasil FL, et al.Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann Neurol. 2013; 74(1):100–8.
Andersen RA, Cui H. Intention, action planning, and decision making in parietal-frontal circuits. Neuron. 2009; 63(5):568–83.
Collin C, Wade D. Assessing motor impaired after stroke: a pilot reliability study. J Neurol Neurosurg Psychiatry. 1990; 53(7):576–9.
Hortal E, Úbeda A, Iáñez E, Azorín JM. Control of a 2 dof robot using a brain-machine interface. Comput Methods Prog Biomed New Methods Human-robot Interaction Med Pract. 2014; 116(2):169–76.
Lum PS, Burgar CG, Shor PC. Evidence for improved muscle activation patterns after retraining of reaching movements with the mime robotic system in subjects with post-stroke hemiparesis. Neural Syst Rehabil Eng IEEE Trans. 2004; 12(2):186–94.
Hara Y. Rehabilitation with functional electrical stimulation in stroke patients. Int J Phys Med Rehabil. 2013; 1(147):2.
Meadmore KL, Hughes A, Freeman CT, Cai Z, Tong D, Burridge JH, et al. Functional electrical stimulation mediated by iterative learning control and 3d robotics reduces motor impairment in chronic stroke. J Neuroeng Rehabil. 2012; 9(1):32–42.
Zariffa J, Kapadia N, Kramer JL, Taylor P, Alizadeh-Meghrazi M, Zivanovic V, et al.Feasibility and efficacy of upper limb robotic rehabilitation in a subacute cervical spinal cord injury population. Spinal Cord. 2012; 50(3):220–6.
Gijbels D, Lamers I, Kerkhofs L, Alders G, Knippenberg E, Feys P. The armeo spring as training tool to improve upper limb functionality in multiple sclerosis: a pilot study. J Neuroeng Rehabil. 2011; 8(5):1–8.
Flash T, Hogan N. The coordination of arm movements: an experimentally confirmed mathematical model. J Neurosci. 1985; 5(7):1688–703.
Ziegler j. G, Nichols NB, Rochester NY. Optimum settings for automatic controllers. Trans ASME. 1942; 64(11):759–68.
Decety J, Lindgren M. Sensation of effort and duration of mentally executed actions. Scand J Psychol. 1991; 32:97–104.
Guger C, Schlögl A, Neuper C, Walterspacher D, Strein T, Pfurtscheller G. Rapid prototyping of an eeg-based brain-computer interface (bci). IEEE Trans Rehabil Eng. 2001; 9(1):49–58.
Davis NJ, Tomlinson SP, Morgan HM. The role of beta-frequency neural oscillations in motor control. J Neurosci. 2012; 32(2):403–4.
Pfurtscheller G, Brunner C, Schlögl A, Lopes da Silva FH. Mu rhythm (de)synchronization and eeg single-trial classification of different motor imagery tasks. NeuroImage. 2006; 31:153–9.
Hong B, Wang Y, Gao X, Gao S. Quantitative eeg analysis methods and clinical applications In: Tong S, Thakor NV, editors. Quantitative EEG Analysis Methods and Clinical Applications. Norwood, MA, USA: Artech House: 2009. p. 51–108.
Iáñez E, Úbeda A, Hortal E, Azorín JM. Mental tasks selection method for a svm-based bci system. In: Proceedings of the 7th Annual IEEE International Systems Conference (SYSCON 2013), Orlando, United States. USA: IEEE: 2013. p. 767–71.
Hortal E, Iáñez E, Úbeda A, Planelles D, Costa A, Azorín JM. Selection of the best mental tasks for a svm-based bci system. In: IEEE International Conference on Systems, Man, and Cybernetics, San Diego, USA. USA: IEEE: 2014. p. 1502–7.
Lotte F, Congedo M, Lcuyer A, Lamarche F, Arnald B. A review of classification algorithms for eeg-based brain-computer interfaces. J Neural Eng. 2007; 4(2):1–13.
Bashashati A, Fatourechi M, Ward RK, Birch GE. A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J Neural Eng. 2007; 4(2):R32–57.
Thome ACG. Svm classifiers - concepts and applications to character recognition In: Ding X, editor. Advances in Character Recognition. Rijeka, Croatia: InTech: 2012. p. 25–50. online.
Planelles D, Hortal E, Costa A, Úbeda A, Iáñez E, Azorín JM. Evaluating classifiers to detect arm movement intention from eeg signals. Sensors. 2014; 14:18172–86.
Pfurtscheller G, da Silva FHL. Event-related eeg/meg synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999; 110:1842–57.
Sejdic E, Fu Y, Pak A, Fairley JA, Chau T. The effects of rhythmic sensory cues on the temporal dynamics of human gait. PLOS ONE. 2012; 7(8):e43104.
Kalcher J, Pfurtscheller G. Discrimination between phase-locked and non-phase-locked event-related eeg activity. Electroencephalogr Clin Neurophysiol. 1995; 94:381–483.
Pfurtscheller G, Kalcher J, Neuper C, Flotzinger D, Pregenzer M. On-line eeg classification during externally paced hand movements using a neural network-based classifier. Electroencephalogr Clin Neurophysiol. 1996; 99:416–25.
Ting CM, Salleh SH, Zainuddin ZM, Bahar A. Spectral estimation of nonstationary eeg using particle filtering with application to event-related desynchronization (erd). IEEE Trans Biomed Eng. 2011; 58(2):321–31.
Hassan A, Niazi I, Jochumsen M, Riaz F, Dremstrup K. Clasification of kinetics of movement for lower limb using covariate shift method for brain computer interface. In: IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP). USA: IEEE: 2014.
Hortal E, IáÑez E, Úbeda A, Perez-Vidal C, Azorín JM. Combining a brain-machine interface and an electrooculography interface to perform pick and place tasks with a robotic arm. Robot Autonomous Syst. 2015; 72:181–8.
Planelles D, Hortal E, Iáñez E, Costa A, Azorín JM. Processing eeg signals to detect intention of upper limb movement. In: Replace, Repair, Restore, Relieve - Bridging Clinical and Engineering Solutions in Neurorehabilitation, Proceedings of the 2nd International Conference on NeuroRehabilitation. Berlin, Heidelberg, Germany: Springer International Publishing: 2014. p. 655–64.