Posts Tagged BCI

[ARTICLE] Classification of EEG signals for wrist and grip movements using echo state network – Full Text

Abstract

Brain-Computer Interface (BCI) is a multi-disciplinary emerging technology being used in medical diagnosis and rehabilitation. In this paper, different techniques of classification and feature extraction are applied to analyse and differentiate the wrist and grip flexion and extension for synchronized stimulation using sensory feedback in neuro-rehabilitation of paralyzed persons. We have used an optimized version of Echo State Network (ESN) to identify as well as differentiate the wrist and grip movements. In this work, the classification accuracy obtained is greater than 96% in a single trial and 93% in discrimination of four movements in real and imagination.

Introduction

The popularity of analysing brain rhythms and its applications in healthcare is evident in rehabilitation engineering. Motor disabilities as a consequence of stroke require rehabilitation process to regain the motor learning and retrieval. The classification of EEG signals obtained by using a low cost Brain Computer Interface (BCI) for wrist and grip movements is used for recovery. Using Movement Related Cortical Potential (MRCP) associated with imaginary movement as detected by the BCI, an external device can be synchronized to provide sensory feedback from electrical stimulation [1]. The timely detection, classification of movement and the real time triggering of the electrical stimulation as a function of brain activity is desirable for neuro-rehabilitation [2,3]. Thus, BCI has an active role in helping out the paralyzed persons who are not able to move their hand or leg [4]. Using BCI system, EEG data is recorded and processed. The acquired data should have the least component of environmental noise and artifacts for effective classification [5]. EEG signals acquired from the invasive method are found to exhibit least noise components and higher amplitude. However, in most applications, a non-invasive method is preferred. The human brain contains a number of neuron networks. EEG provides a measurement of brain activity as voltage fluctuations which are recorded as a result of ionic current within neurons present inside the brain [6]. Many people have motor disabilities due to the nerve system breakdown or accidental failure of nerve system. There are different methods to resolve this problem, e.g. neuro-prosthetics (neural prosthetics) and BCI [3,79]. In neuro-prosthetics, a solution of the problem is in the form of connecting brain nerve system with the device and in BCI connecting brain nerve system with computer [2]. BCI produce a communication between brain and computer via EEG, ECOG or MEG signals. These signals contain information of any of our body activity [10]. Moreover, in addition to neuro-rehabilitation, assistive robotics and brain control mobile robots also utilizes similar technologies as reported recently [11,12]. The signal processing of these low amplitude and noisy EEG signals require special care during data acquisition and filtering. After recording EEG measurements, these signals are processed via filtration, feature extraction, and classification. Simple first or second order Chebyshev or Butterworth filter can be used as a low pass, high pass or a notch filter. Some features can be extracted by using one of the techniques from time analysis, frequency analysis, time-frequency analysis or time-space-frequency analysis [13,14]. Extracted EEG signal further classify by using one of the techniques like LDA, QDA, SVM, KNN etc. [15,16].

We aim to classify the wrist and grip movements using EEG signals. This research will be helpful for convalescence of persons having disabilities in wrist or grip. Our work is based on offline data-sets, in which the EEG data is collected multiple times from 4 subjects. We present the following major contributions in this paper: First, the differentiation between the wrist and grip movements has been performed by using imaginary data as well as the real movements. Secondly, we have tested multiple algorithms for feature extraction and classification and used ESN with optimized parameters for best results. This paper is organized as follows: section 2 describes a low-cost BCI setup for EEG, section 3 deals with the DAQ protocol, section 4 explains the echo state network and its optimization while section 5 discusses results obtained in this research. Section 6 concludes the paper.

Brain Computer Interface Design

Brain-Computer Interface (BCI) design requires a multi-disciplinary approach for engineers to observe EEG data. Today, a number of sensing platforms are available which provide a low-cost solution for high-resolution data acquisition. Developing a BCI interface requires a two-step approach namely the acquisition and the real-time processing. In off-line processing, the only requirement is to do the acquisition. The data is acquired via a wireless network from the pick-off electrodes arranged on the scalp of the subjects [17]. One such available system is Emotiv, which is easy to install and use. Emotiv headset with 14 electrodes and 2 reference electrodes, CMD and DRL, is used to collect data as shown in Figure 1. All electrodes have potential with respect to the reference electrode. Emotiv headset is a non-invasive device to collect the EEG data as preferred in most of the diagnosis and rehabilitation applications [18].

biomedres-Emotiv-EEG

Figure 1. Emotiv EEG acquisition using P-300 standard.

It is important to understand the EEG signal format and frequency content for pre-processing and offline classification. Table 1 shows some of the indications of physical movements and mind actions associated with different brain rhythms in somewhat overlapping frequency bands. It is obvious that the motor imagery tasks are associated with the μ-rhythm in 8-13 Hz frequency band [19].

Rhythm Frequency
(Hz)
Indication Diagnosis
Δ 0-4 Deep sleep stage Hypoglycaemia, Epilepsy
υ 4-7 Initial sleep stage
α 8-12 Closure of eyes Migraine, Dementia
β 12-30 Busy/Anxious thinking Encephalopathies, Tonic seizures
γ 30-100 Cognitive/motor function
µ 8-13 Motor imagery tasks Autism Spectrum Disorder

Table 1. Brain frequency bands and their significance.

biomedres-Grip-movement

 

Continue —> Classification of EEG signals for wrist and grip movements using echo state network

, , , , , , , , ,

Leave a comment

[Abstract] BCI controlled neuromuscular electrical stimulation enables sustained motor recovery in chronic stroke victims – PDF

R. Leeb1,2,#, A. Biasiucci2,#, T. Schmidlin1 , T. Corbet2 , P. Vuadens3 , JdR. Millán2,*

  1. Center for Neuroprosthetics (CNP), École Polytechnique Fédérale de Lausanne, Sion, Switzerland;
  2. Chair in Brain-Machine Interface (CNBI), École Polytechnique Fédérale de Lausanne, Geneva, Switzerland;
  3. SUVACare – Clinique Romande de Réadaptation, Sion, Switzerland

Equal contributions; * Campus Biotech, Chemin des Mines 9, CH-1202 Geneva, Switzerland; E-mail: jose.millan@epfl.ch

Introduction: Recently, it has been shown that brain-computer interfaces (BCI) can be used in stroke rehabilitation to decode motor attempts from brain signals and to trigger movements of the paralyzed limb [1]. Among other available practices in rehabilitation, neuromuscular electrical stimulation (NMES) is often used to directly engage muscles on the affected parts of the body during physical therapy. Nevertheless, the benefits of a combined approach, to directly link the brain intention with a muscular response, are not yet fully validated. In this abstract, we report first results of a BCI-NMES system for stroke rehabilitation.

Material and Methods: Up to now, we enrolled 18 chronic stroke victims (minimum 10 months past the incident) suffering from an impairment of the upper limb in a randomized controlled clinical trial. Half of the subjects were assigned to the BCI group and half to a “sham” group, whereby the criteria such as motor impairment –measured via the Fugl-Meyer scale for upper extremity (FM) score–, age, time since incident and lesion location were balanced. Generally, the experimental protocol consisted of three different phases: (i) patients underwent a preevaluation to check the motor capabilities, to characterize the initial state of the brain and to calibrate the BCI classifier (see BCI details in [2]). (ii) In the following weeks, they were trained with an online BCI twice a week for 10 sessions (45 to 90 minutes including setup). (iii) Finally, they performed a post-experimental screening to determine changes in EEG patterns and in motor functions following the treatment, and a 6-month follow-up to evaluate the sustainment. Patients in the BCI group received NMES of the extensor digitorum muscles triggered by the BCI detecting the intention of movement at the cortical level (modulation of the sensorimotor rhythm in the contralateral motor cortex). For patients in the sham group the NMES was not correlated with the brain activity. All subjects were asked to attempt to open their paretic hand (full sustained finger extension) with the aim of activating the NMES upon detection of a suitable sensorimotor rhythms (Fig. 1-a). Subjects in the two groups (BCI and sham) received comparable amount of NMES.

Results: Remarkably, subjects in the BCI group improved their motor function (post minus pre) by 8.6±5.0 FM points (which is more than the minimal clinical change of 5.25 FM points), while those in the sham group improved only by 2.4±3.4 FM points (Fig. 1-b). As expected, the features used by the BCI classifier were mostly located over the affected hemisphere and the motor cortex (see topographic presentation in Fig. 1-c).

Discussion: We hypothesize that the motor improvement in the BCI group (in contrast to the sham group) is triggered by the tight timed and functional link between the intended action in the brain, and the executed and perceived motor action, through the activation of the body’s natural efferent and afferent pathways.

Significance: In our randomized controlled trial, we demonstrate that the modulation of sensorimotor rhythms driving contingent neuromuscular stimulation is more effective than sham stimulation with active motor attempt, and that the proposed therapy dosage produces a clinically important recovery in chronic stroke survivors having a moderate-to-severe motor impairment.

References: [1] Ramos-Murguialday A, et al. Brain-machine interface in chronic stroke rehabilitation. Ann Neurol, 74(1):100-108, 2013. [2] Leeb R, et al. Transferring brain-computer interfaces beyond the laboratory: Successful application control for motor-disabled users. Artif Intell Med, 59: 121-132, 2013.

Download PDF file

, , , , , , , , ,

Leave a comment

[VIDEO] Bryan Baxter – Sensorimotor Rhythm BCI with TDCS Alters Task Performance – YouTube

Δημοσιεύτηκε στις 25 Οκτ 2016

This talk was given at the BCI Meeting 2016 at Asilomar Conference Grounds on May 31st, 2016.

, , , , ,

Leave a comment

[Abstract] Brain-computer interfaces for communication and rehabilitation : Nature Reviews Neurology

Abstract

Brain–computer interfaces (BCIs) use brain activity to control external devices, thereby enabling severely disabled patients to interact with the environment. A variety of invasive and noninvasive techniques for controlling BCIs have been explored, most notably EEG, and more recently, near-infrared spectroscopy. Assistive BCIs are designed to enable paralyzed patients to communicate or control external robotic devices, such as prosthetics; rehabilitative BCIs are designed to facilitate recovery of neural function. In this Review, we provide an overview of the development of BCIs and the current technology available before discussing experimental and clinical studies of BCIs. We first consider the use of BCIs for communication in patients who are paralyzed, particularly those with locked-in syndrome or complete locked-in syndrome as a result of amyotrophic lateral sclerosis. We then discuss the use of BCIs for motor rehabilitation after severe stroke and spinal cord injury. We also describe the possible neurophysiological and learning mechanisms that underlie the clinical efficacy of BCIs.

Figures

  1. General framework of brain-computer interface (BCI) systems.
    Figure 1
  2. Use of a brain-computer interface in severe chronic stroke.
    Figure 2

References

  1. Wyrwicka, W. & Sterman, M. B. Instrumental conditioning of sensorimotor cortex EEG spindles in the waking cat. Physiol. Behav. 3, 703707 (1968).
  2. Kamiya, J. in Altered states of consciousness. (ed Tart, C.) 519529 (New York: Wiley, 1969).
  3. Fetz, E. E. & Baker, M. A. Operantly conditioned patterns on precentral unit activity and correlated responses in adjacent cells and contralateral muscles. J. Neurophysiol. 36, 179204 (1973).
  4. Vidal, J.-J. Toward direct brain-computer communication. Annu. Rev. Biophys. Bioeng. 2, 157180 (1973).
    The first paper describing a brain computer interface and the hypothetical learning mechanisms involved.
  5. Sterman, M. B., Wyrwicka, W. & Roth, S. Electrophysiological correlates and neural substrates of alimentary behavior in the cat. Ann. NY Acad. Sci. 157, 723739 (1969).
  6. Sterman, M. & Friar, L. Suppression of seizures in epileptic Following on sensorimotor EEG feedback training. Electroencephalogr. Clin. Neurophysiol. 33, 8995 (1972).
  7. Lubar, J. F. & Shouse, M. N. EEG and behavioral changes in a hyperkinetic child concurrent with training of the sensorimotor rhythm (SMR) – A preliminary report. Biofeedback Self Regul. 1, 293306 (1976).
  8. Sterman, M. B. & Macdonald, L. R. Effects of central cortical EEG feedback training on incidence of poorly controlled seizures. Epilepsia 19, 207222 (1978).
  9. Chapin, J. K., Moxon, K. A., Markowitz, R. S. & Nicolelis, M. A. L. Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex. Nat. Neurosci. 2, 664670 (1999).
  10. Donoghue, J. P. Connecting cortex to machines: recent advances in brain interfaces. Nat. Neurosci. 5, 10851088 (2002).
  11. Nicolelis, M. A. L. Actions from thoughts. Nature 409, 403407 (2001).
  12. Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S. & Schwartz, A. B. Cortical control of a prosthetic arm for self-feeding. Nature 453, 10981101 (2008).
  13. Taylor, D. M., Tillery, S. I. H. & Schwartz, A. B. Direct Cortical Control of 3D Neuroprosthetic Devices. Sci. 296, 18291832 (2002).
  14. Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A. & Shenoy, K. V. A high-performance brain-computer interface. Nature 442, 195198 (2006).
  15. Wessberg, J. et al. Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 408, 361365 (2000).
  16. Serruya, M. D., Hatsopoulos, N. G., Paninski, L., Fellows, M. R. & Donoghue, J. P. Brain-machine interface: Instant neural control of a movement signal. Nature 416, 141142 (2002).
  17. Carmena, J. M. et al. Learning to control a brain–machine interface for reaching and grasping by primates. PLoS Biol. 1, e2 (2003).
    This paper provides the most advanced and detailed neurophysiological analysis of the neuronal mechanisms behind brain–computer interface control of complex movements.

  18. Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164171 (2006).
  19. Donoghue, J. P., Nurmikko, A., Black, M. & Hochberg, L. R. Assistive technology and robotic control using motor cortex ensemble-based neural interface systems in humans with tetraplegia. J. Physiol. 579, 603611 (2007).
  20. Birbaumer, N., Ramos Murguialday, A., Weber, C. & Montoya, P. Chapter 8 neurofeedback and brain-computer Interface: clinical applications. Int. Rev. Neurobiol. 86, 107117 (2009).
  21. Fuchs, T., Birbaumer, N., Lutzenberger, W., Gruzelier, J. H. & Kaiser, J. Neurofeedback treatment for attention-deficit / hyperactivity disorder in children: a comparison with methylphenidate. Appl. Psychophysiol. Biofeedback 28, 112 (2003).
  22. Monastra, V. J. et al. Electroencephalographic biofeedback in the treatment of attention-deficit / hyperactivity disorder. J. Neurother. 9, 534 (2006).
  23. Kotchoubey, B. et al. Modification of slow cortical potentials in patients with refractory epilepsy: a controlled outcome study. Epilepsia 42, 406416 (2001).
  24. Gilja, V. et al. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21, 11421145 (2015).
  25. Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372375 (2012).
    The first paper describing multidemensional movement control of an arm–hand robotic device using an implanted microelectrode array in the primary motor cortex of a paralyzed patient.

  26. Jarosiewicz, B. et al. Virtual typing by people with tetraplegia using a stabilized, self-calibrating intracortical brain-computer interface. IEEE BRAIN Gd. Challenges Conf. Washington, DC 7, 111 (2014).
  27. Pfurtscheller, G., Müller, G. R., Pfurtscheller, J., Gerner, H. J. & Rupp, R. ‘Thought ‘ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci. Lett. 351, 3336 (2003).
  28. Caria, A., Sitaram, R. & Birbaumer, N. Real-time fMRI: a tool for local brain regulation. Neuroscientist. 18, 487501 (2012).
  29. Chaudhary, U., Birbaumer, N. & Curado, M. R. Brain-machine interface (BMI) in paralysis. Ann. Phys. Rehabil. Med. 58, 913 (2015).
  30. Nijboer, F. et al. An auditory brain–computer interface (BCI). J. Neurosci. Methods 167, 4350 (2008).
  31. Chatterjee, A., Aggarwal, V., Ramos, A., Acharya, S. & Thakor, N. V. A brain-computer interface with vibrotactile biofeedback for haptic information. J. Neuroeng. Rehabil. 4, 112(2007).
  32. Lugo, Z. R. et al. A vibrotactile p300-based brain-computer interface for consciousness detection and communication. Clin. EEG Neurosci. 45, 1421 (2014).
  33. Bansal, A. K., Truccolo, W., Vargas-Irwin, C. E. & Donoghue, J. P. Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: spikes, multiunit activity, and local field potentials. J. Neurophysiol. 107, 13371355 (2012).
  34. Flint, R. D., Wright, Z. A., Scheid, M. R. & Slutzky, M. W. Long term, stable brain machine interface performance using local field potentials and multiunit spikes. J. Neural Eng. 10, 056005 (2013).
  35. So, K., Dangi, S., Orsborn, A. L., Gastpar, M. C. & Carmena, J. M. Subject-specific modulation of local field potential spectral power during brain-machine interface control in primates. J. Neural Eng. 11, 026002 (2014).
  36. Mehring, C. et al. Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex. J. Physiol. Paris 98, 498506 (2004).
  37. Georgopoulos, A. P., Schwartz, A. B. & Kettner, R. E. Neuronal population coding of movement direction. Science 233, 14161419 (1986).
  38. Georgopoulos, A. P. & Kettner, R. E. & Schwartz, A. B. Primate motor cortex and free arm movements to visual targets in three-dimensional space. II. Coding of the direction of movement by a neuronal population. J. Neurosci. 8, 29282937 (1988).
  39. Serruya, M., Hatsopoulos, N., Paninski, L., Fellows, M. R. & Donoghue, J. P. Brain-machine interface: Instant neural control of a movement signal. Nature 416, 121142 (2002).
  40. Leuthardt, E. C., Schalk, G., Wolpaw, J. R., Ojemann, J. G. & Moran, D. W. A brain–computer interface using electrocorticographic signals in humans. J. Neural Eng. 1, 6371(2004).
  41. Felton, E. a, Wilson, J. A., Williams, J. C. & Garell, P. C. Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases. J. Neurosurg. 106, 495500 (2007).
  42. Clancy, K. B., Koralek, A. C., Costa, R. M., Feldman, D. E. & Carmena, J. M. Volitional modulation of optically recorded calcium signals during neuroprosthetic learning. Nat. Neurosci. 17, 807809 (2014).
  43. Birbaumer, N., Elbert, T., Canavan, A. & Rockstroh, B. Slow potentials of the cerebral cortex and behavior. Physiol. Rev. 70, 141 (1990).
  44. Kubler, A. et al. Brain-computer communication: self regulation of slow cortical potentials for verbal communication. Arch. Phys. Med. Rehabil. 82, 15331539 (2001).
  45. Birbaumer, N., Hinterberger, T., Kübler, A. & Neumann, N. The thought-translation device (TTD): neurobehavioral mechanisms and clinical outcome. IEEE Trans. Neural Syst. Rehabil. Eng. 11, 120123 (2003).
  46. Pfurtscheller, G. & Aranibar, A. Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. Electroencephalogr. Clin. Neurophysiol. 46, 138146 (1979).
  47. Kübler, A. et al. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology 64, 17751777 (2005).
  48. Wolpaw, J. R. et al. Brain-computer interfaces for communication and control. Clin. Neurophysiol. 113, 767791 (2002).
  49. Farwell, L. A. & Donchin, E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70, 510523(1988).
  50. Kübler, A. et al. A brain-computer interface controlled auditory event-related potential (p300) spelling system for locked-in patients. Ann. NY Acad. Sci. 1157, 90100 (2009).
  51. Halder, S. et al. An auditory oddball brain-computer interface for binary choices. Clin. Neurophysiol. 121, 516523 (2010).
  52. Pires, G., Nunes, U. & Castelo-Branco, M. Statistical spatial filtering for a P300-based BCI: Tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis. J. Neurosci. Methods 195, 270281 (2011).
  53. Sellers, E. W. & Donchin, E. A P300-based brain-computer interface: Initial tests by ALS patients. Clin. Neurophysiol. 117, 538548 (2006).
  54. Sellers, E. W., Vaughan, T. M. & Wolpaw, J. R. A brain-computer interface for long-term independent home use. Amyotroph. Lateral Scler. 11, 449455 (2010).
  55. Lesenfants, D. et al. An independent SSVEP-based brain-computer interface in locked-in syndrome. J. Neural Eng. Neural Eng. 11, 035002 (2014).
  56. Zhu, D., Bieger, J., Molina, G. G. & Aarts, R. M. A survey of stimulation methods used in SSVEP-based BCIs. Comput. Intell. Neurosci. http://dx.doi.org/10.1155/2010/702357(2010).
  57. Chavarriaga, R. & Millán, J. del R. Learning from EEG error-related potentials in noninvasive brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 18, 381388 (2010).
  58. Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150157 (2001).
  59. Birbaumer, N., Ruiz, S. & Sitaram, R. Learned regulation of brain metabolism. Trends Cogn. Sci. 17, 295302 (2013).
    An extensive review of basic and clinical neurofeedback studies using learning of metabolic brain resonses (BOLD or oxygenation) and the effects on behaviour and cognition.
  60. DeCharms, R. C. et al. Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage 21, 436443 (2004).
  61. Rota, G., Handjaras, G., Sitaram, R., Birbaumer, N. & Dogil, G. Reorganization of functional and effective connectivity during real-time fMRI-BCI modulation of prosody processing. Brain Lang. 117, 123132 (2011).
  62. Weiskopf, N. et al. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. Neuroimage 19, 577586 (2003).
  63. Yoo, S. S. et al. Brain computer interface using fMRI: spatial navigation by thoughts. Neuroreport 15, 15911595 (2004).
  64. Birbaumer, N. et al. Deficient fear conditioning in psychopathy: a functional magnetic resonance imaging study. Arch. Gen. Psychiatry 62, 799805 (2005).
  65. Linden, D. E. J. et al. Real-time self-regulation of emotion networks in patients with depression. PLoS One http://dx.doi.org/10.1371/journal.pone.0038115 (2012).
  66. Li, X. et al. Volitional reduction of anterior cingulate cortex activity produces decreased cue craving in smoking cessation: A preliminary real-time fMRI study. Addict. Biol. 18, 739748(2013).
  67. Chaudhary, U., Hall, M., DeCerce, J., Rey, G. & Godavarty, A. Frontal activation and connectivity using near-infrared spectroscopy: verbal fluency language study. Brain Res. Bull. 84, 197205 (2011).
  68. Chaudhary, U. et al. Motor response investigation in individuals with cerebral palsy using near infrared spectroscopy: pilot study. Appl. Opt. 53, 503510 (2014).
  69. Obrig, H. NIRS in clinical neurology – a ‘promising’ tool? Neuroimage 85, 535546 (2014).
  70. Gallegos-Ayala, G. et al. Brain communication in a completely locked-in patient using bedside near-infrared spectroscopy. Neurology 82, 19301932 (2014).
    The first report of a controlled case study with BCI in a completely paralyzed, locked-in patient restoring communication.

  71. Naito, M. et al. A communication means for totally locked-in ALS patients based on changes in cerebral blood volume measured with near-infrared light. IEICE Trans. Inf. Syst. E90D, 10281037 (2007).
  72. Birbaumer, N. et al. A spelling device for the paralysed. Nature 398, 297298 (1999).
  73. Ramos-Murguialday, A. et al. Brain-machine interface in chronic stroke rehabilitation: a controlled study. Ann. Neurol. 74, 100108 (2014).
  74. Birbaumer, N., Murguialday, A. R. & Cohen, L. Brain-computer interface in paralysis. Curr. Opin. Neurol. 21, 634638 (2008).
  75. Chou, S. M. & Norris, F. H. Amyotrophic lateral sclerosis: Lower motor neuron disease spreading to upper motor neurons. Muscle Nerve 16, 864869 (1993).
  76. Bauer, G., Gerstenbrand, F. & Rumpl, E. Varieties of the Locked-in Syndrome. J. Neurol.221, 7791 (1979).
  77. Beukelman, D., Fager, S. & Nordness, A. Communication support for people with ALS. Neurol. Res. Int. 2011, 714693 (2011).
  78. Beukelman, D. & Mirenda, P. Augmentative & alternative communication: Supporting children & adults with complex communication needs. (Paul, H. Brookes, Baltimore, MD, 2005).
  79. Birbaumer, N. & Cohen, L. G. Brain-computer interfaces: communication and restoration of movement in paralysis. J. Physiol. 579, 621636 (2007).
  80. Kennedy, P. R. & Bakay, R. A. Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 9, 17071711 (1998).
  81. Kennedy, P. R., Bakay, R. A., Moore, M. M., Adams, K. & Goldwaithe, J. Direct control of a computer from the human central nervous system. IEEE Trans. Rehabil. Eng. 8, 198202(2000).
  82. Kennedy, P. et al. Using human extra-cortical local field potentials to control a switch. J. Neural Eng. 1, 7277 (2004).
  83. Wilhelm, B., Jordan, M. & Birbaumer, N. Communication in locked-in syndrome: effects of imagery on salivary pH. Neurology 67, 534535 (2006).
  84. Murguialday, A. R. et al. Transition from the locked in to the completely locked-in state: a physiological analysis. Clin. Neurophysiol. 122, 925933 (2011).
  85. Birbaumer, N. Breaking the silence: brain-computer interfaces (BCI) for communication and motor control. Psychophysiology 43, 517532 (2006).
  86. Kübler, A. & Birbaumer, N. Brain-computer interfaces and communication in paralysis: extinction of goal directed thinking in completely paralysed patients? Clin. Neurophysiol. 119, 26582666 (2008).
  87. Wolpaw, J. R. & McFarland, D. J. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc. Natl Acad. Sci. USA 101, 1784917854 (2004).
  88. Bai, O., Lin, P., Huang, D., Fei, D. Y. & Floeter, M. K. Towards a user-friendly brain-computer interface: initial tests in ALS and PLS patients. Clin. Neurophysiol. 121, 12931303 (2010).
  89. Thorns, J. et al. Movement initiation and inhibition are impaired in amyotrophic lateral sclerosis. Exp. Neurol. 224, 389394 (2010).
  90. Birbaumer, N., Piccione, F., Silvoni, S. & Wildgruber, M. Ideomotor silence: the case of complete paralysis and brain-computer interfaces (BCI). Psychol. Res. 76, 183191 (2012).
  91. Hinterberger, T. et al. Neuronal mechanisms underlying control of a brain – computer interface. Eur. J. Neurosci. 21, 31693181 (2005).
  92. Hinterberger, T. et al. Voluntary brain regulation and communication with electrocorticogram signals. Epilepsy Behav. 13, 300306 (2008).
  93. Koralek, A. C. et al. Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature 483, 331335 (2012).
  94. Dworkin, B. R. & Miller, N. E. Failure to replicate visceral learning in the acute curarized rat preparation. Behav. Neurosci. 100, 299314 (1986).
    This paper describes the failure to establish instrumental learning of physiological responses in the curarized rat and possible reasons for this problem.
  95. Stocco, A., Lebiere, C. & Anderson, J. R. Conditional routing of information to the cortex: a model of the basal ganglia’s role in cognitive coordination. Psychol. Rev. 117, 541574(2010).
  96. Birbaumer, N. & Chaudhary, U. Learning from brain control: clinical application of brain–computer interfaces. e-Neuroforum 6, 8795 (2015).
  97. Furdea, A. et al. A new (semantic) reflexive brain-computer interface: in search for a suitable classifier. J. Neurosci. Methods 203, 233240 (2012).
  98. Ruf, C. A., De Massari, D., Wagner-Podmaniczky, F., Matuz, T. & Birbaumer, N. Semantic conditioning of salivary pH for communication. Artif. Intell. Med. 59, 18 (2013).
  99. De Massari, D. et al. Brain communication in the locked-in state. Brain 136, 19892000(2013).
  100. Lulé, D. et al. Brain responses to emotional stimuli in patients with amyotrophic lateral sclerosis (ALS). J. Neurol. 254, 519527 (2007).
  101. Lulé, D. et al. Life can be worth living in locked-in syndrome. Prog. Brain Res. 177, 339351(2009).
  102. Lulé, D. et al. Quality of life in fatal disease: the flawed judgement of the social environment. J. Neurol. 260, 28362843 (2013).
  103. Chaudhary, U. & Birbaumer, N. Communication in locked-in state after brainstem stroke: a brain- computer-interface approach. Ann. Transl. Med. 3, 24 (2015).
  104. Simeral, J. D., Kim, S. P., Black, M. J., Donoghue, J. P. & Hochberg, L. R. Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. J. Neural Eng. 8, 025027 (2011).
  105. Kübler, A. et al. Self-regulation of slow cortical potentials in completely paralyzed human patients. Neurosci. Lett. 252, 171174 (1998).
  106. Piccione, F. et al. P300-based brain computer interface: reliability and performance in healthy and paralysed participants. Clin. Neurophysiol. 117, 531537 (2006).
  107. Sellers, E. W., Ryan, D. B. & Hauser, C. K. Noninvasive brain-computer interface enables communication after brainstem stroke. Sci. Transl. Med. 6, 257re7 (2014).
  108. Cirstea, M. C., Ptito, A. & Levin, M. F. Arm reaching improvements with short-term practice depend on the severity of the motor deficit in stroke. Exp. Brain Res. 152, 476488 (2003).
  109. Young, J. & Forster, A. Review of stroke rehabilitation. BMJ 334, 8690 (2007).
  110. Saka, O., McGuire, A. & Wolfe, C. Cost of stroke in the United Kingdom. Age Ageing 38, 2732 (2008).
  111. Langhorne, P., Bernhardt, J. & Kwakkel, G. Stroke rehabilitation. Lancet 377, 16931702(2015).
  112. Hendricks, H. T., van Limbeek, J., Geurts, A. C. & Zwarts, M. J. Motor recovery after stroke: a systematic review of the literature. Arch. Phys. Med. Rehabil. 83, 16291637 (2002).
  113. Ward, N. S. & Cohen, L. G. Mechanisms underlying recovery of motor function after stroke. Arch. Neurol. 61, 18441848 (2004).
  114. Taub, E., Uswatte, G. & Pidikiti, R. Constraint-induced movement therapy: a new family of techniques with broad application to physical rehabilitation – a clinical review. J. Rehabil. Res. Dev. 36, 237251 (1999).
  115. Wolf, S. L. et al. Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. JAMA 296, 20952104(2006).
  116. Buch, E. R. et al. Parietofrontal integrity determines neural modulation associated with grasping imagery after stroke. Brain 135, 596614 (2012).
  117. Belda-Lois, J.-M. et al. Rehabilitation of gait after stroke: a review towards a top-down approach. J. Neuroeng. Rehabil. 8, 66 (2011).
  118. Chollet, F. et al. Fluoxetine for motor recovery after acute ischaemic stroke (FLAME): a randomised placebo-controlled trial. Lancet Neurol. 10, 123130 (2011).
  119. Savitz, S. I. et al. Stem cells as an emerging paradigm in stroke 3: enhancing the development of clinical trials. Stroke 45, 634639 (2014).
  120. Ganguly, K., Dimitrov, D. F., Wallis, J. D. & Carmena, J. M. Reversible large-scale modification of cortical networks during neuroprosthetic control. Nat. Neurosci. 14, 662667(2011).
  121. Gulati, T. et al. Robust neuroprosthetic control from the stroke perilesional cortex. J. Neurosci. 35, 86538661 (2015).
  122. Nishimura, Y., Perlmutter, S. I., Eaton, R. W. & Fetz, E. E. Spike-timing-dependent plasticity in primate corticospinal connections induced during free behavior. Neuron 80, 13011309(2013).
    This paper describes the neurophysiological bases of BCI applications in spinal cord injury.

  123. Lucas, T. H. & Fetz, E. E. Myo-cortical crossed feedback reorganizes primate motor cortex output. J. Neurosci. 33, 52615274 (2013).
  124. Ang, K. K. et al. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front. Neuroeng. http://dx.doi.org/10.3389/fneng.2014.00030 (2014).
  125. Ono, T. et al. Brain-computer interface with somatosensory feedback improves functional recovery from severe hemiplegia due to chronic stroke. Front. Neuroeng. http://dx.doi.org/10.3389/fneng.2014.00019 (2014).
  126. Pichiorri, F. et al. Brain–computer interface boosts motor imagery practice during stroke recovery. Ann. Neurol. 77, 851865 (2015).
  127. Kasahara, K., DaSalla, C. S., Honda, M. & Hanakawa, T. Neuroanatomical correlates of brain–computer interface performance. Neuroimage 110, 95100 (2015).
  128. Bensmaia, S. J. & Miller, L. E. Restoring sensorimotor function through intracortical interfaces: progress and looming challenges. Nat. Rev. Neurosci. 15, 313325 (2014).
  129. Ren, X. et al. Enhanced low-latency detection of motor intention from EEG for closed-loop brain-computer interface applications. Biomed. Eng. IEEE Trans. 61, 288296 (2014).
  130. Jiang, N., Gizzi, L., Mrachacz-Kersting, N., Dremstrup, K. & Farina, D. A brain–computer interface for single-trial detection of gait initiation from movement related cortical potentials. Clin. Neurophysiol. 126, 154159 (2015).
  131. Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557564 (2013).
  132. Ouzký, M. Towards concerted efforts for treating and curing spinal cord injury (Council of Europe Parliamentary Assembly document 9401). https://assembly.coe.int/nw/xml/XRef/X2H-Xref-ViewHTML.asp?FileID=9680&lang=en (2002)
  133. Van Den Berg, M. E., Castellote, J. M., Mahillo-Fernandez, I. & De Pedro-Cuesta, J.Incidence of spinal cord injury worldwide: a systematic review. Neuroepidemiology 34, 184192 (2010).
  134. Wolpaw, J. R. The complex structure of a simple memory. Trends Neurosci. 20, 588594(1997).
  135. Wang, W. et al. An electrocorticographic brain interface in an individual with tetraplegia. PLoS ONE http://dx.doi.org/10.1371/journal.pone.0055344 (2013).
  136. Pfurtscheller, G., Müller, G. R., Pfurtscheller, J. & Gerner, H. J. & Rupp, R. ‘Thought’ – Control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia. Neurosci. Lett. 351, 3336 (2003).
  137. Nguyen, J. S., Su, S. W. & Nguyen, H. T. Experimental study on a smart wheelchair system using a combination of stereoscopic and spherical vision. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2013, 45974600 (2013).
  138. Kasashima-Shindo, Y. et al. Brain–computer interface training combined with transcranial direct current stimulation in patients with chronic severe hemiparesis: proof of concept study. J. Rehabil. Med. 47, 318324 (2015).
  139. Enzinger, C. et al. Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training. Exp. Brain Res. 190, 215223 (2008).
  140. King, C. E. et al. The feasibility of a brain-computer interface functional electrical stimulation system for the restoration of overground walking after paraplegia. J. Neuroeng. Rehabil. 12, 80 (2015).
  141. Pfurtscheller, G., Guger, C., Müller, G., Krausz, G. & Neuper, C. Brain oscillations control hand orthosis in a tetraplegic. Neurosci. Lett. 292, 211214 (2000).
    The first paper demonstrating noninvasive brain control using a sensorimotor rhythm brain–computer interface in a high spinal cord patient.

  142. Courtine, G. & Bloch, J. Defining Ecological Strategies in Neuroprosthetics. Neuron 86, 2933 (2015).
  143. van den Brand, R. et al. Restoring voluntary control of locomotion after paralyzing spinal cord injury. Science 336, 11821185 (2012).
  144. Combaz, A. et al. A comparison of two spelling brain-computer interfaces based on visual P3 and SSVEP in locked-in syndrome. PLoS ONE http://dx.doi.org/10.1371/journal.pone.0073691 (2013).
  145. Bardin, J. C. et al. Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury. Brain 134, 769782(2011).
  146. Monti, M. M. et al. Willful modulation of brain activity in disorders of consciousness. N. Engl. J. Med. 362, 579589 (2010).
  147. Schnakers, C. et al. Detecting consciousness in a total locked-in syndrome: an active event-related paradigm. Neurocase 15, 271277 (2009).
  148. Lulé, D. et al. Probing command following in patients with disorders of consciousness using a brain-computer interface. Clin. Neurophysiol. 124, 101106 (2013).

Source: Brain-computer interfaces for communication and rehabilitation : Nature Reviews Neurology : Nature Research

, , , , ,

Leave a comment

[ARTICLE] Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis – Full Text

Abstract

Background

The use of Brain–Computer Interface (BCI) technology in neurorehabilitation provides new strategies to overcome stroke-related motor limitations. Recent studies demonstrated the brain’s capacity for functional and structural plasticity through BCI. However, it is not fully clear how we can take full advantage of the neurobiological mechanisms underlying recovery and how to maximize restoration through BCI. In this study we investigate the role of multimodal virtual reality (VR) simulations and motor priming (MP) in an upper limb motor-imagery BCI task in order to maximize the engagement of sensory-motor networks in a broad range of patients who can benefit from virtual rehabilitation training.

Methods

In order to investigate how different BCI paradigms impact brain activation, we designed 3 experimental conditions in a within-subject design, including an immersive Multimodal Virtual Reality with Motor Priming (VRMP) condition where users had to perform motor-execution before BCI training, an immersive Multimodal VR condition, and a control condition with standard 2D feedback. Further, these were also compared to overt motor-execution. Finally, a set of questionnaires were used to gather subjective data on Workload, Kinesthetic Imagery and Presence.

Results

Our findings show increased capacity to modulate and enhance brain activity patterns in all extracted EEG rhythms matching more closely those present during motor-execution and also a strong relationship between electrophysiological data and subjective experience.

Conclusions

Our data suggest that both VR and particularly MP can enhance the activation of brain patterns present during overt motor-execution. Further, we show changes in the interhemispheric EEG balance, which might play an important role in the promotion of neural activation and neuroplastic changes in stroke patients in a motor-imagery neurofeedback paradigm. In addition, electrophysiological correlates of psychophysiological responses provide us with valuable information about the motor and affective state of the user that has the potential to be used to predict MI-BCI training outcome based on user’s profile. Finally, we propose a BCI paradigm in VR, which gives the possibility of motor priming for patients with low level of motor control.

Continue —> Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: a within-subject analysis | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 2 MI-BCI training conditions. (a) VRMP: the user has to perform motor priming by mapping his/her hand movements into the virtual environment. (b) VR: the user has to perform training through simultaneous motor action observation and MI, before moving to the MI task were he/she has to control the virtual hands through MI. (c) Control: MI training with standard feedback through arrows-and-bars

, , , , , , ,

Leave a comment

[ARTICLE] The Cybathlon promotes the development of assistive technology for people with physical disabilities – Full Text

Abstract

Background

The Cybathlon is a new kind of championship, where people with physical disabilities compete against each other at tasks of daily life, with the aid of advanced assistive devices including robotic technologies. The first championship will take place at the Swiss Arena Kloten, Zurich, on 8 October 2016.

The idea

Six disciplines are part of the competition comprising races with powered leg prostheses, powered arm prostheses, functional electrical stimulation driven bikes, powered wheelchairs, powered exoskeletons and brain-computer interfaces. This commentary describes the six disciplines and explains the current technological deficiencies that have to be addressed by the competing teams. These deficiencies at present often lead to disappointment or even rejection of some of the related technologies in daily applications.

Conclusion

The Cybathlon aims to promote the development of useful technologies that facilitate the lives of people with disabilities. In the long run, the developed devices should become affordable and functional for all relevant activities in daily life.

Keywords

Competition, Championship ,Prostheses, Exoskeletons ,Functional electrical stimulation, Wheelchairs, Brain computer interfaces

Background

Millions of people worldwide rely on orthotic, prosthetic, wheelchairs and other assistive devices to improve their qualities of life. In the US there live more than 1.6 million people with limb amputations [1] and the World Health Organization estimates the number of wheelchair users to about 65 million people worldwide [2]. Unfortunately, current assistive technology does not address their needs in an ideal fashion. For instance, wheelchairs cannot climb stairs, arm prostheses do not enable versatile hand functions, and power supplies of many orthotic and prosthetic devices are limited. There is a need to further push the development of assistive devices by pooling the efforts of engineers and clinicians to develop improved technologies, together with the feedback and experiences of the users of the technologies.

The Cybathlon is a new kind of championship with the aim of promoting the development of useful technologies. In contrast with the Paralympics, where parathletes aim to achieve maximum performance, at the Cybathlon, people with physical disabilities compete against each other at tasks of daily life, with the aid of advanced assistive devices including robotic technologies. Most current assistive devices lack satisfactory function; people with disabilities are often disappointed, and thus do not use and accept the technology. Rejection can be due to a lack of communication between developers, people with disabilities, therapists and clinicians, which leads to a disregard of user needs and requirements. Other reasons could be that the health status, level of lesion or financial situation of the potential user are so severe that she or he is unable to use the available technologies. Furthermore, barriers in public environments make the use of assistive technologies often very cumbersome or even impossible.

Six disciplines are part of the competition, addressing people with either limb paralysis or limb amputations. The six disciplines comprise races with powered leg prostheses, powered arm prostheses, functional electrical stimulation (FES) driven bikes, powered wheelchairs and powered exoskeletons (Fig. 1). The sixth discipline is a racing game with virtual avatars that are controlled by brain-computer interfaces (BCI). The functional and assistive devices used can be prototypes developed by research labs or companies, or commercially available products. The competitors are called pilots, as they have to control a device that enhances their mobility. The teams each consist of a pilot together with scientists and technology providers, making the Cybathlon also a competition between companies and research laboratories. As a result there are two awards for each winning team in each discipline: a medal for the person who is controlling the device and a cup for the provider of the device (i.e. the company or the lab).

Fig. 1 Arena with four parallel race tracks designed for the exoskeleton competition. The pilots start at the left and have to overcome six obstacles with increasing difficulty level

Continue —> The Cybathlon promotes the development of assistive technology for people with physical disabilities | Journal of NeuroEngineering and Rehabilitation | Full Text

Download PDF

Download ePub

, , , , , , , , , ,

Leave a comment

[Abstract] Review of functional near-infrared spectroscopy in neurorehabilitation – Neurophotonics – SPIE

Abstract

We provide a brief overview of the research and clinical applications of near-infrared spectroscopy (NIRS) in the neurorehabilitation field. NIRS has several potential advantages and shortcomings as a neuroimaging tool and is suitable for research application in the rehabilitation field.

As one of the main applications of NIRS, we discuss its application as a monitoring tool, including investigating the neural mechanism of functional recovery after brain damage and investigating the neural mechanisms for controlling bipedal locomotion and postural balance in humans. In addition to being a monitoring tool, advances in signal processing techniques allow us to use NIRS as a therapeutic tool in this field.

With a brief summary of recent studies investigating the clinical application of NIRS using motor imagery task, we discuss the possible clinical usage of NIRS in brain–computer interface and neurofeedback.

NPH_3_3_031414_f001.png

Source: Review of functional near-infrared spectroscopy in neurorehabilitation | Neurophotonics | SPIE

, , , , , , ,

Leave a comment

[ARTICLE] Paired Associative Stimulation using Brain-Computer Interfaces for Stroke Rehabilitation: A Pilot study – Full Text PDF

Abstract

Conventional therapies do not provide paralyzed patients with closed-loop sensorimotor integration for motor rehabilitation. Paired associative stimulation (PAS) uses braincomputer interface (BCI) technology to monitor patients’ movement imagery in real-time, and utilizes the information to control functional electrical stimulation (FES) and bar feedback for complete sensorimotor closed loop. To realize this approach, we introduce the recoveriX system, a hardware and software platform for PAS. After 10 sessions of recoveriX training, one stroke patient partially regained control of dorsiflexion in her paretic wrist. A controlled group study is planned with a new version of the recoveriX system, which will use a new FES system and an avatar instead of bar feedback.

I. INTRODUCTION

In conventional rehabilitation therapy, patients are often asked to try to move the paretic limb, or imagine its movement, while a functional electrical stimulator (FES), physiotherapist, or robotic device helps them perform the desired movement. However, if patients cannot perform the movement without help, there is no objective way to determine whether each patient is actually performing the desired motor imagery task. This dissociation between motor commands and sensory feedback may explain why the therapy does not significantly induce the reorganization of the patients’ brain around their lesioned area. To close the feedback loop for paralyzed patients, we used bar feedback and FES based on their motor imagery (MI) [1]–[3]. This paired associative stimulation (PAS) is an important factor for motor recovery [4]–[10]. Neural networks are facilitated when the presynaptic and postsynaptic neurons are both active…

Full Text PDF

, , , , , ,

Leave a comment

[Proceedings] Virtual reality and brain computer interface in neurorehabilitation – Full Text

Abstract

The potential benefit of technology to enhance recovery after central nervous system injuries is an area of increasing interest and exploration. The primary emphasis to date has been motor recovery/augmentation and communication. This paper introduces two original studies to demonstrate how advanced technology may be integrated into subacute rehabilitation. The first study addresses the feasibility of brain computer interface with patients on an inpatient spinal cord injury unit. The second study explores the validity of two virtual environments with acquired brain injury as part of an intensive outpatient neurorehabilitation program. These preliminary studies support the feasibility of advanced technologies in the subacute stage of neurorehabilitation. These modalities were well tolerated by participants and could be incorporated into patients’ inpatient and outpatient rehabilitation regimens without schedule disruptions. This paper expands the limited literature base regarding the use of advanced technologies in the early stages of recovery for neurorehabilitation populations and speaks favorably to the potential integration of brain computer interface and virtual reality technologies as part of a multidisciplinary treatment program.

The yearly incidence of traumatic brain injury (TBI) (∼1.7 million), acquired brain injury (∼900,000), and spinal cord injury (SCI) (∼12,000) in the US fails to adequately reflect the long-term impact and annual societal cost, which may exceed $100 billion a year (15). As advances in medical care are improving survival rates in these populations, the need for a multidisciplinary approach focusing on long-term outcomes, secondary complications, and quality of life is magnified (3, 6). This multidisciplinary approach strongly aligns with key aspects of the World Health Organization’s International Classification of Functioning, Disability, and Health Model, where the primary health conditions must be conceptualized in relation to environmental and contextual factors with emphasis upon improving function (7) (Figure 1). Advanced technologies such as brain computer interface (BCI), which uses brain patterns to help patients bypass injuries that impede motor or verbal responses, and virtual reality (VR) have shown potential as viable treatment tools in the rehabilitation setting (811); however, the application of advanced technologies in neurorehabilitation is not systematic, and studies to support their use in clinical settings remain limited (1214). This prompted our group to begin exploratory studies into the feasibility and utility of off-the-shelf BCI and VR technologies with neurorehabilitation populations. This paper introduces two original studies to demonstrate how advanced technology may be integrated into the subacute phase of central nervous system recovery. Approval to complete the studies was obtained from the hospital’s institutional review board.

Continue —> Virtual reality and brain computer interface in neurorehabilitation

, , ,

Leave a comment

[ARTICLE] On Usage of EEG Brain Control for Rehabilitation of Stroke Patients – Full Text PDF

ABSTRACT

This paper demonstrates rapid prototyping of a stroke rehabilitation system consisting of an interactive 3D virtual reality computer game environment interfaced with an EEG headset for control and interaction using brain waves. The system is intended for training and rehabilitation of partially monoplegic stroke patients and uses lowcost commercial-off-the-shelf products like the Emotiv EPOC EEG headset and the Unity 3D game engine.

A number of rehabilitation methods exist that can improve motor control and function of the paretic upper limb in stroke survivors. Unfortunately, most of these methods are commonly characterised by a number of drawbacks that can limit intensive treatment, including being repetitive, uninspiring, and labour intensive; requiring one-on-one manual interaction and assistance from a therapist, often for several weeks; and involve equipment and systems that are complex and expensive and cannot be used at home but only in hospitals and institutions by trained personnel.

Inspired by the principles of mirror therapy and game-stimulated rehabilitation, we have developed a first prototype of a game-like computer application that tries to avoid these drawbacks. For rehabilitation purposes, we deprive the patient of the view of the paretic hand while being challenged with controlling a virtual hand in a simulated 3D game environment only by means of EEG brain waves interfaced with the computer.

Whilst our system is only a first prototype, we hypothesise that by iteratively improving its design through refinements and tuning based on input from domain experts and testing on real patients, the system can be tailored for being used together with a conventional rehabilitation programme to improve patients’ ability to move the paretic limb much in the same vain as mirror therapy.

Our proposed system has several advantages, including being game-based, customisable, adaptive, and extendable. In addition, when compared with conventional rehabilitation methods, our system is extremely low-cost and flexible, in particular because patients can use it in the comfort of their homes, with little or no need for professional human assistance. Preliminary tests are carried out to highlight the potential of the proposed rehabilitation system, however, in order to measure its efficiency in rehabilitation, the system must first be improved and then run through an extensive field test with a sufficiently large group of patients and compared with a control group.

Continue —> Full Text PDF

 

, , , , , , , ,

Leave a comment

%d bloggers like this: