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BCIs and physical medicine and rehabilitation: The future is now
Annals of Physical and Rehabilitation Medicine 58 (2015) 1–2
Available online at
ScienceDirect www.sciencedirect.com
Editorial
BCIs and physical medicine and rehabilitation: The future is now A R T I C L E I N F O
Keywords: Brain Computer Interface BCI Rehabilitation
The (critical) development of Brain Computer Interfaces (BCIs) in the last two decades raised new hopes for disabled patients. In healthy persons, BCIs offer promising scopes for application in the field of entertainment or video gaming. Those commercial potential perspectives for applications in the digital industry explain a renewed interest in a subject until now highly confidential. Generally speaking, in medical applications, BCI involves: the recording of brain signals generated by the patient, as he/she performs a particular mental or ‘‘real’’ task; the decoding and transformation of this signal into a specific action; and a feedback given to the patient. This technology relies either on implanted electrodes – socalled ‘‘invasive BCI’’ – or on external devices recording brain signal – so-called ‘‘non-invasive BCI’’. Most studies on patients have been conducted with non-invasive BCI but rapid progresses in the implanted materials and enlargements in Deep Brain Stimulation indications may change the future and the way we consider those technologies. This special issue of the Annals of PRM is dedicated to the applications of BCI technologies in neuro-rehabilitation. Although in its infancy, several recent experiments showed the potential usefulness of BCI to compensate or restore motor and cognitive impairment (for a review see: [1]). One of the most obvious applications is the use of BCI to control a robotic orthesis or another device (like a wheelchair for example) as a substitute for motor loss. Following animal studies showing that a monkey was able to modulate his cortical activity to control http://dx.doi.org/10.1016/j.rehab.2014.12.002 1877-0657/ß 2015 Published by Elsevier Masson SAS.
a prosthetic device [2,3], the same approach has been applied to patients with tetraplegia (see: [4,5]). After a stroke, BCI could also favor motor recovery through the enhancement of brain plasticity or the re-equilibration of inter-hemispheric imbalance (for a review in this special issue see Van Dokkum et al. [6]). In another article of this issue, Chaudhary et al. [7] report their contribution to this field showing how BCI can improve recovery of hand function after stroke using the brain signal to activate a functional electrical stimulation (FES) delivered to the paretic muscles. BCI may also be advantageously utilized in order to restore or enhance communication in patients with severe neurological impairments. In this special issue, Chaudhary et al. [7] reviewed the current literature using non-invasive BCI tools – electroencephalography (EEG) and near-infrared spectroscopy (NIRS) – to restore a functional communication in patients suffering from amyotrophic lateral sclerosis (ALS) with complete paralysis. In another study published in this issue, Mattout et al. [8] described the ‘‘P300 speller’’, one of the most advanced BCI communicative tool based on the detection of a P300 wave when the attended letter appears on a computer screen. In this latter article, the authors screened the challenges and technical gaps to apply efficiently this assistance to paralyzed patients. The same P300 signal has been incorporated in a BCI row-column scanning board with verbal and non-verbal instructions adapted to individuals with cerebral palsy (see Scherer et al. [9] in this issue). The establishment of a reliable communication is also one of the main goals for patients with disorders of consciousness (DOC). The willful modulation of brain activity recorded in patients considered in a vegetative state [10] led some authors to develop BCI applications for communication purposes using either the electroencephalography (EEG) signal (e.g. [11]) or fMRI paradigms (e.g. [12]). These articles are critically
2
Editorial / Annals of Physical and Rehabilitation Medicine 58 (2015) 1–2
analyzed with regard to the potential applications at the patient’s bedside (see Luaute´ et al. [13] in this issue). Altogether, there are now some evidences that BCI technology can improve the independence of patients with devastating neurological disorders. However, most of the current literature is based on proof of principle studies. More research is still needed to find the best candidates and the optimal delay since insult, to improve the efficiency of signal detection and decoding, to facilitate the portability of the devices and the cost of these techniques. Moreover, as pointed out by Nijboer [14] in this issue, BCIs should be developed to be usable rather than only reliable, and BCIs will also need to be competitive with existing alternatives in terms of efficiency and user experience and satisfaction. In this perspective, it appears crucial that the development of BCIs and transfer of this technology to patients involves rehabilitation of professionals at the early stage of design processes. This technology is an excellent example of the urgent need of translational studies involving neuroscientists, engineers and clinicians. PRM doctors should not miss the train. . . Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. References [1] Daly JJ, Wolpaw JR. Brain–computer interfaces in neurological rehabilitation. Lancet Neurol 2008;7:1032–43. [2] Carmena JM, et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol 2003;1:193–208. [3] Velliste M, Perel S, Spalding MC, Whitford AS, Schwartz AB. Cortical control of a prosthetic arm for self-feeding. Nature 2008;453:1098–101. [4] Hochberg LR, Serruya MD, Friehs GM, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006;442:164–71. [5] Leeb R, Friedman D, Muller-Putz GR, Scherer R, Slater M, Pfurtscheller G. Selfpaced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput Intell Neurosci 2007;2007:79642. [6] Van Dokkum L, Ward T, Laffont I. Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke. Ann Phys Rehabil Med 2015 [In press].
[7] Chaudhary U, Birbaumer N, Curado MR. Brain-machine interfaces (BMI) in paralysis. Ann Phys Rehabil Med 2015 [In press]. [8] Mattout J, Perrin M, Bertrand O, Maby E. Improving BCI performance through co-adaptation: applications to the P300 speller. Ann Phys Rehabil Med 2015 [In press]. [9] Scherer R, Billinger M, Wagner J, Schwarz A, Hettich DT, Bolinger E, et al. Thought-based row-column scanning communication board for individuals with cerebral palsy. Ann Phys Rehabil Med 2015 [In press]. [10] Owen AM, Coleman MR, Boly M, Davis MH, Laureys S, Pickard JD. Detecting awareness in the vegetative state. Science 2006;313:1402. [11] Cruse D, Chennu S, Chatelle C, et al. Bedside detection of awareness in the vegetative state: a cohort study. Lancet 2011;378:2088–94. [12] Monti MM, Vanhaudenhuyse A, Coleman MR, et al. Willful modulation of brain activity in disorders of consciousness. N Engl J Med 2010;362:579–89. [13] Luaute´ J, Morlet D, Mattout J. BCI in patients with disorders of consciousness: clinical perspectives. Ann Phys Rehabil Med 2015 [In press]. [14] Nijboer F. Barriers for technology transfer of Brain-Computer Interfaces as assistive technology. Ann Phys Rehabil Med 2015 [In press].
Jacques Luaute´ MD, PhDa,b,c,* Hospices Civils de Lyon, Hoˆpital Henry Gabrielle, Mouvement et Handicap, Saint-Genis Laval, 69230, France b Integrative, Multisensory, Perception, Action and Cognition Team, Lyon Neuroscience Research Center, INSERM U1028-CNRS UMR5292, Lyon 69000, France c Universite´ de Lyon, Universite´ Lyon 1, Villeurbanne, 69100, France a
Isabelle Laffont MD, PhDd,e Movement to Health, Euromov, Universite´ Montpellier 1, France e De´partement de MPR, CHRU de Montpellier, France
d
author. Re´e´ducation Neurologique, Hoˆpital Henry Gabrielle, 20, Route de Vourles, 69230 Saint-Genis Laval, France. Tel.: +33 4 78 86 50 23; fax: +33 4 78 86 50 30 E-mail address: [email protected] (J. Luaute´) *Corresponding
Received 30 November 2014 Accepted 18 December 2014 Available online xxx
Available online at
ScienceDirect www.sciencedirect.com
Editorial
BCIs and physical medicine and rehabilitation: The future is now A R T I C L E I N F O
Keywords: Brain Computer Interface BCI Rehabilitation
The (critical) development of Brain Computer Interfaces (BCIs) in the last two decades raised new hopes for disabled patients. In healthy persons, BCIs offer promising scopes for application in the field of entertainment or video gaming. Those commercial potential perspectives for applications in the digital industry explain a renewed interest in a subject until now highly confidential. Generally speaking, in medical applications, BCI involves: the recording of brain signals generated by the patient, as he/she performs a particular mental or ‘‘real’’ task; the decoding and transformation of this signal into a specific action; and a feedback given to the patient. This technology relies either on implanted electrodes – socalled ‘‘invasive BCI’’ – or on external devices recording brain signal – so-called ‘‘non-invasive BCI’’. Most studies on patients have been conducted with non-invasive BCI but rapid progresses in the implanted materials and enlargements in Deep Brain Stimulation indications may change the future and the way we consider those technologies. This special issue of the Annals of PRM is dedicated to the applications of BCI technologies in neuro-rehabilitation. Although in its infancy, several recent experiments showed the potential usefulness of BCI to compensate or restore motor and cognitive impairment (for a review see: [1]). One of the most obvious applications is the use of BCI to control a robotic orthesis or another device (like a wheelchair for example) as a substitute for motor loss. Following animal studies showing that a monkey was able to modulate his cortical activity to control http://dx.doi.org/10.1016/j.rehab.2014.12.002 1877-0657/ß 2015 Published by Elsevier Masson SAS.
a prosthetic device [2,3], the same approach has been applied to patients with tetraplegia (see: [4,5]). After a stroke, BCI could also favor motor recovery through the enhancement of brain plasticity or the re-equilibration of inter-hemispheric imbalance (for a review in this special issue see Van Dokkum et al. [6]). In another article of this issue, Chaudhary et al. [7] report their contribution to this field showing how BCI can improve recovery of hand function after stroke using the brain signal to activate a functional electrical stimulation (FES) delivered to the paretic muscles. BCI may also be advantageously utilized in order to restore or enhance communication in patients with severe neurological impairments. In this special issue, Chaudhary et al. [7] reviewed the current literature using non-invasive BCI tools – electroencephalography (EEG) and near-infrared spectroscopy (NIRS) – to restore a functional communication in patients suffering from amyotrophic lateral sclerosis (ALS) with complete paralysis. In another study published in this issue, Mattout et al. [8] described the ‘‘P300 speller’’, one of the most advanced BCI communicative tool based on the detection of a P300 wave when the attended letter appears on a computer screen. In this latter article, the authors screened the challenges and technical gaps to apply efficiently this assistance to paralyzed patients. The same P300 signal has been incorporated in a BCI row-column scanning board with verbal and non-verbal instructions adapted to individuals with cerebral palsy (see Scherer et al. [9] in this issue). The establishment of a reliable communication is also one of the main goals for patients with disorders of consciousness (DOC). The willful modulation of brain activity recorded in patients considered in a vegetative state [10] led some authors to develop BCI applications for communication purposes using either the electroencephalography (EEG) signal (e.g. [11]) or fMRI paradigms (e.g. [12]). These articles are critically
2
Editorial / Annals of Physical and Rehabilitation Medicine 58 (2015) 1–2
analyzed with regard to the potential applications at the patient’s bedside (see Luaute´ et al. [13] in this issue). Altogether, there are now some evidences that BCI technology can improve the independence of patients with devastating neurological disorders. However, most of the current literature is based on proof of principle studies. More research is still needed to find the best candidates and the optimal delay since insult, to improve the efficiency of signal detection and decoding, to facilitate the portability of the devices and the cost of these techniques. Moreover, as pointed out by Nijboer [14] in this issue, BCIs should be developed to be usable rather than only reliable, and BCIs will also need to be competitive with existing alternatives in terms of efficiency and user experience and satisfaction. In this perspective, it appears crucial that the development of BCIs and transfer of this technology to patients involves rehabilitation of professionals at the early stage of design processes. This technology is an excellent example of the urgent need of translational studies involving neuroscientists, engineers and clinicians. PRM doctors should not miss the train. . . Disclosure of interest The authors declare that they have no conflicts of interest concerning this article. References [1] Daly JJ, Wolpaw JR. Brain–computer interfaces in neurological rehabilitation. Lancet Neurol 2008;7:1032–43. [2] Carmena JM, et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol 2003;1:193–208. [3] Velliste M, Perel S, Spalding MC, Whitford AS, Schwartz AB. Cortical control of a prosthetic arm for self-feeding. Nature 2008;453:1098–101. [4] Hochberg LR, Serruya MD, Friehs GM, et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006;442:164–71. [5] Leeb R, Friedman D, Muller-Putz GR, Scherer R, Slater M, Pfurtscheller G. Selfpaced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Comput Intell Neurosci 2007;2007:79642. [6] Van Dokkum L, Ward T, Laffont I. Brain computer interfaces for neurorehabilitation – its current status as a rehabilitation strategy post-stroke. Ann Phys Rehabil Med 2015 [In press].
[7] Chaudhary U, Birbaumer N, Curado MR. Brain-machine interfaces (BMI) in paralysis. Ann Phys Rehabil Med 2015 [In press]. [8] Mattout J, Perrin M, Bertrand O, Maby E. Improving BCI performance through co-adaptation: applications to the P300 speller. Ann Phys Rehabil Med 2015 [In press]. [9] Scherer R, Billinger M, Wagner J, Schwarz A, Hettich DT, Bolinger E, et al. Thought-based row-column scanning communication board for individuals with cerebral palsy. Ann Phys Rehabil Med 2015 [In press]. [10] Owen AM, Coleman MR, Boly M, Davis MH, Laureys S, Pickard JD. Detecting awareness in the vegetative state. Science 2006;313:1402. [11] Cruse D, Chennu S, Chatelle C, et al. Bedside detection of awareness in the vegetative state: a cohort study. Lancet 2011;378:2088–94. [12] Monti MM, Vanhaudenhuyse A, Coleman MR, et al. Willful modulation of brain activity in disorders of consciousness. N Engl J Med 2010;362:579–89. [13] Luaute´ J, Morlet D, Mattout J. BCI in patients with disorders of consciousness: clinical perspectives. Ann Phys Rehabil Med 2015 [In press]. [14] Nijboer F. Barriers for technology transfer of Brain-Computer Interfaces as assistive technology. Ann Phys Rehabil Med 2015 [In press].
Jacques Luaute´ MD, PhDa,b,c,* Hospices Civils de Lyon, Hoˆpital Henry Gabrielle, Mouvement et Handicap, Saint-Genis Laval, 69230, France b Integrative, Multisensory, Perception, Action and Cognition Team, Lyon Neuroscience Research Center, INSERM U1028-CNRS UMR5292, Lyon 69000, France c Universite´ de Lyon, Universite´ Lyon 1, Villeurbanne, 69100, France a
Isabelle Laffont MD, PhDd,e Movement to Health, Euromov, Universite´ Montpellier 1, France e De´partement de MPR, CHRU de Montpellier, France
d
author. Re´e´ducation Neurologique, Hoˆpital Henry Gabrielle, 20, Route de Vourles, 69230 Saint-Genis Laval, France. Tel.: +33 4 78 86 50 23; fax: +33 4 78 86 50 30 E-mail address: [email protected] (J. Luaute´) *Corresponding
Received 30 November 2014 Accepted 18 December 2014 Available online xxx