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EDITORIAL |
1 NINDS, National Institutes of Health, Human Cortical Physiology Section, Building 10, Room 5N226, 10 Center Drive, MSC 1430, Bethesda, MD 20892, USA
2 Institute of Medical Psychology and Behavioural Neurobiology, University of Tuebingen, Tuebingen, Germany
Email:cohenl{at}ninds.nih.govniels.birbaumer{at}uni-tuebingen.de
This issue of The Journal of Physiology addresses a theme that is at the crossroads of different disciplines including neurophysiology, neuroimaging, neurorehabilitation, brain plasticity and engineering: brain–computer interfaces (BCIs). Emerging work in these fields has led in the last decade to the exploration of the general hypothesis that it is possible to ‘translate’ brain activity into specific motions of mechanical devices or computer applications. This general idea, thought of as science fiction a few decades ago, is now seriously considered and engages an increasing number of scientists in multidisciplinary fields worldwide. The goals of controlling motion of a mechanical arm or choosing letters or words to be displayed on a computer screen utilizing invasively or non-invasively recorded physiological signals from a behaving brain encompass important engineering issues. But perhaps the most fascinating challenge that will define the progress of this field will be the ability of physiologists to record and decode brain activity with enough detail to translate volitional decisions of an individual into specific instructions to mechanical devices or computer interfaces. Success in this endeavour may lead in the future to applications like the utilization of these physiological advances to allow paralysed individuals to control movements of prostheses attached to paralysed arms or legs, wheelchair movement control, and communication in locked-in patients with others. Clearly, the clinical relevance of this physiological challenge cannot be overstated.
The invited contributions of this issue address the description of the various types of neurophysiological signals utilized to control BCI devices and the research that is under way to improve the detection tools, data processing and control of BCI applications. Eberhard Fetz focuses on the discussion of the extent to which neural signals can be volitionally controlled, particularly measured by the activity of cortical neurons in for example operant conditioning paradigms using biofeedback. He also discusses the limits in the degree of accuracy of control obtained from physiological signals recorded in recent human studies (Fetz, 2007). Andy Schwartz discusses how behavioural aspects of action are represented in motor cortical activity, focusing on the extent to which kinematic parameters of movement relate to neural activity in the motor cortex (Schwartz, 2007). Fetz's and Schwartz's articles review the physiological principles that represent the foundations of human BCI applications.
John Donoghue's article describes human applications of these principles using signals recorded directly from the motor cortex with intracortical microelectrodes (Donoghue et al. 2007). The representations of body parts deafferented or deefferented by lesions like stroke or spinal cord injury persist in the motor cortex even years after injury. These signals could be used by paralysed individuals to operate a range of mechanical or computer devices. Jonathan Wolpaw's article focuses on the use of physiological signals recorded non-invasively from the brain (Wolpaw, 2007). He discusses the importance of the principles of cooperativity of multiple cortical regions to generate a particular behaviour and the involvement of adaptive plasticity on the choice of the particular physiological signals to control BCI devices. Niels Birbaumer focuses on the link between physiological and clinical applications of non-invasive brain–computer interfaces like brain communication in paralysis and motor restoration in stroke (Birbaumer & Cohen, 2007). Finally, Bruce Dobkin's article assesses the impact of BCI work performed so far in the basic and clinical domains on the field of clinical neurorehabilitation, as well as the future challenges posed by these patient populations (Dobkin, 2007).
Altogether, these papers provide a comprehensive review of the physiological principles of brain–computer interfaces, the reality, the physiological challenges and the expected future developments, as well as their clinical implications in the fields of neurorehabilitation and motor control.
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Dobkin BH (2007). Brain–computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol 579, 637–642.
Donoghue JP, Nurmikko A, Black M & Hochberg LR (2007). Assistive technology and robotic control using primary motor cortex ensemble-based neural interface systems in humans with tetraplegia. J Physiol 579, 603–611.
Fetz EE (2007). Volitional control of neural activity: implications for brain–computer interfaces. J Physiol 579, 571–579.
Schwartz AB (2007). Useful signals from motor cortex. J Physiol 579, 581–601.
Wolpaw JR (2007). Brain–computer interfaces as new brain output pathways. J Physiol 579, 613–619.
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