Brain-Machine Interfaces for Motor Control: A Guide for Neuroscience Clinicians

Abstract

Brain-machine interfaces (BMIs) have been defined as devices that detect intent-typically intended movement-from brain activity, and translate it into an output action, such as control of a cursor on a screen or a robotic arm. There is no doubt that the potential of BMI technology to augment normal motor performance is vast, a fact which has attracted both attention and funding from a variety of sources including United States military. [1][2][3] For clinicians in the neurosciences, however, BMI technology could have the potential to assist in motor recovery or maintenance. At present, several forms of BMI devices, in varying stages of development, are being investigated and used to treat conditions that cause severe motor impairments, including amyotrophic lateral sclerosis (ALS), stroke, and spinal cord injury. [4][5][6][7][8][9] The preliminary experiences of several groups with motor BMI applications in clinical settings have generated justifiable enthusiasm about the promise of future BMI applications. Such enthusiasm, however, is tempered by a definite need for further advances in a number of specific aspects ABSTRACT: With the growing interdependence between medicine and technology, the prospect of connecting machines to the human brain is rapidly being realized. The field of neuroprosthetics is transitioning from the proof of concept stage to the development of advanced clinical treatments. In one area of brain-machine interfaces (BMIs) related to the motor system, also termed 'motor neuroprosthetics', research successes with implanted microelectrodes in animals have demonstrated immense potential for restoring motor deficits. Early human trials have also begun, with some success but also highlighting several technical challenges. Here we review the concepts and anatomy underlying motor BMI designs, review their early use in clinical applications, and offer a framework to evaluate these technologies in order to predict their eventual clinical utility. Ultimately, we hope to help neuroscience clinicians understand and participate in this burgeoning field.RÉSUMÉ: Guide sur les interface cerveau-machine pour le contrôle moteur destiné aux cliniciens en neurosciences. Étant donné l'interdépendance croissante de la médecine et de la technologie, la perspective de brancher des machines au cerveau humain se réalise rapidement. Le domaine de la neuroprothétique évolue maintenant de l'étape de la validation du principe à celui du développement de traitements cliniques de pointe. Dans le domaine des interfaces cerveau-machine (ICM) pour le système moteur, aussi connu sous le nom de "neuroprothétique motrice", les succès de la recherche sur les microélectrodes implantées chez des animaux ont démontré un potentiel immense pour pallier aux déficits moteurs. Des essais préliminaires chez les humains ont remporté un certain succès, mais ils ont aussi mis en évidence plusieurs défis techniques. Nous revoyons ici les concepts et l'anatomie pertinents à la conception des ICM motrices et leur utilisation préliminaire en cl...

Emerging Therapeutic Enhancement Enabling Health Technologies and Their Discourses: What Is Discussed within the Health Domain?

“…Nearly 40% of articles discuss people with disabilities as the main target for BMI intervention; most conceptualize people with disabilities as patients in need of a means to improve communication [ 119 , 120 , 121 , 122 ], and motor skills [ 121 , 123 , 124 , 125 ]. The common theme among these articles is the desire to give physically disabled people a “quasi-normal life” [ 126 ].…”

Towards an EEG-based brain-computer interface for online robot control

“…We broadly classify these efforts into two categories: BCIs based on event-related (de)synchronization (ERD or ERS) [4,21,30,32,33,35,40], and BCIs based on steady-state visual evoked potentials (SSVEPs) [16,23,26,36,38]. Then, we discuss some works that use Emotiv EPOC EEG headsets [5,6,11,29,41]. …”

“…BCIs based on steadystate visual evoked potentials (SSVEPs) have also been significantly researched [16,23,26,36,38], but SSVEP BCIs do not work for some patients [27,28]. EEG-based BCIs have been studied as well [5,6,11,29,41], but most of them are not under an online processing. Exploring EEG headsets to control cyber-physical systems is not new.…”

Who Needs to Fit in? Who Gets to Stand out? Communication Technologies Including Brain-Machine Interfaces Revealed from the Perspectives of Special Education School Teachers Through an Ableism Lens

“…As BMI technology continues to develop and advance, its application is expanding beyond its initial target audience of disabled people, toward general and public use, providing opportunities for non-disabled people to enhance their abilities and gain more control of their environment [10,11,21]. The fusion of human and machine for the voluntary control of external devices is gaining momentum in research development [22]. It is opening up new possibilities for entertainment applications, like the gaming industry [4,5,23].…”