Ethical considerations in the use of brain-computer interfaces

Open Medicine ◽  
2013 ◽  
Vol 8 (6) ◽  
pp. 720-724 ◽  
Author(s):  
Emilia Mikołajewska ◽  
Dariusz Mikołajewski
Keyword(s):  
Nervous System ◽  
Clinical Practice ◽  
Clinical Application ◽  
Public Interest ◽  
Brain Computer Interfaces ◽  
Ethical Considerations ◽  
Computer Interfaces ◽  
Planning And Management ◽  
Further Development

AbstractNervous system disorders are among the most severe disorders. Significant breakthroughs in contemporary clinical practice may provide brain-computer interfaces (BCIs) and neuroprostheses (NPs). The aim of this article is to investigate the extent to which the ethical considerations in the clinical application of brain-computer interfaces and associated threats are being identified. Ethical considerations and implications may significantly influence further development of BCIs and NPs. Moreover, there is significant public interest in supervising this development. Awareness of BCIs’ and NPs’ threats and limitations allow for wise planning and management in further clinical practice, especially in the area of long-term neurorehabilitation and care.

CNS Stimulant Controversies

1989 ◽  
Vol 23 (4) ◽  
pp. 497-502 ◽  
Author(s):  
Florence Levy
Keyword(s):  
Central Nervous System ◽  
Animal Model ◽  
Nervous System ◽  
High Dose ◽  
Ethical Considerations ◽  
Hyperactivity Disorder ◽  
Central Nervous System Stimulant ◽  
Stimulant Medications ◽  
Cns Stimulant

Controversies in the use of central nervous system stimulant medications in children with attention deficit hyperactivity disorder are discussed. Diagnostic issues, age of optimal use, side effects, effects on learning and ethical considerations are current issues. An animal model for the effects of chronic long-term high dose regimes is proposed.

Learning from brain control: clinical application of brain–computer interfaces

e-Neuroforum ◽  
2015 ◽  
Vol 21 (4) ◽  
Author(s):  
Niels Birbaumer ◽  
Ujwal Chaudhary
Keyword(s):  
Clinical Application ◽  
Motor Function ◽  
Brain Activity ◽  
Chronic Stroke ◽  
Skill Learning ◽  
Brain Computer Interfaces ◽  
Brain Machine Interfaces ◽  
Positive Effects ◽  
Computer Interfaces ◽  
Brain Signals

AbstractBrain-computer interfaces (BCI) use neuroelectric and metabolic brain activity to activate peripheral devices and computers without mediation of the motor system. In order to activate the BCI patients have to learn a certain amount of brain control. Self-regulation of brain activity was found to follow the principles of skill learning and instrumental conditioning. This review focuses on the clinical application of brain-computer interfaces in paralyzed patients with locked-in syndrome and completely locked-in syndrome (CLIS). It was shown that electroencephalogram (EEG)-based brain-computer interfaces allow selection of letters and words in a computer menu with different types of EEG signals. However, in patients with CLIS without any muscular control, particularly of eye movements, classical EEG-based brain-computer interfaces were not successful. Even after implantation of electrodes in the human brain, CLIS patients were unable to communicate. We developed a theoretical model explaining this fundamental deficit in instrumental learning of brain control and voluntary communication: patients in complete paralysis extinguish goal-directed responseoriented thinking and intentions. Therefore, a reflexive classical conditioning procedure was developed and metabolic brain signals measured with near infrared spectroscopy were used in CLIS patients to answer simple questions with a “yes” or “no”-brain response. The data collected so far are promising and show that for the first time CLIS patients communicate with such a BCI system using metabolic brain signals and simple reflexive learning tasks. Finally, brain machine interfaces and rehabilitation in chronic stroke are described demonstrating in chronic stroke patients without any residual upper limb movement a surprising recovery of motor function on the motor level as well as on the brain level. After extensive combined BCI training with behaviorally oriented physiotherapy, significant improvement in motor function was shown in this previously intractable paralysis. In conclusion, clinical application of brain machine interfaces in well-defined and circumscribed neurological disorders have demonstrated surprisingly positive effects. The application of BCIs to psychiatric and clinical-psychological problems, however, at present did not result in substantial improvement of complex behavioral disorders.

scholarly journals Post-stroke Rehabilitation of Severe Upper Limb Paresis in Germany – Toward Long-Term Treatment With Brain-Computer Interfaces

2021 ◽  
Vol 12 ◽  
Author(s):  
Cornelius Angerhöfer ◽  
Annalisa Colucci ◽  
Mareike Vermehren ◽  
Volker Hömberg ◽  
Surjo R. Soekadar
Keyword(s):  
Upper Limb ◽  
Hand Function ◽  
Treatment Strategies ◽  
Term Treatment ◽  
Brain Computer Interfaces ◽  
Stroke Patients ◽  
Computer Interfaces ◽  
Long Term Treatment ◽  
Upper Limb Paresis

Severe upper limb paresis can represent an immense burden for stroke survivors. Given the rising prevalence of stroke, restoration of severe upper limb motor impairment remains a major challenge for rehabilitation medicine because effective treatment strategies are lacking. Commonly applied interventions in Germany, such as mirror therapy and impairment-oriented training, are limited in efficacy, demanding for new strategies to be found. By translating brain signals into control commands of external devices, brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs) represent promising, neurotechnology-based alternatives for stroke patients with highly restricted arm and hand function. In this mini-review, we outline perspectives on how BCI-based therapy can be integrated into the different stages of neurorehabilitation in Germany to meet a long-term treatment approach: We found that it is most appropriate to start therapy with BCI-based neurofeedback immediately after early rehabilitation. BCI-driven functional electrical stimulation (FES) and BMI robotic therapy are well suited for subsequent post hospital curative treatment in the subacute stage. BCI-based hand exoskeleton training can be continued within outpatient occupational therapy to further improve hand function and address motivational issues in chronic stroke patients. Once the rehabilitation potential is exhausted, BCI technology can be used to drive assistive devices to compensate for impaired function. However, there are several challenges yet to overcome before such long-term treatment strategies can be implemented within broad clinical application: 1. developing reliable BCI systems with better usability; 2. conducting more research to improve BCI training paradigms and 3. establishing reliable methods to identify suitable patients.

Neuroprostheses as an Element of an Eclectic Approach to Intervention in Neurorehabilitation

2014 ◽  
pp. 101-115
Author(s):  
Emilia Mikołajewska
Keyword(s):  
Clinical Practice ◽  
Clinical Applications ◽  
The Novel ◽  
Brain Computer Interfaces ◽  
Specific Approach ◽  
Computer Interfaces ◽  
Eclectic Approach ◽  
Methods And Techniques ◽  
Practice Development ◽  
The Way

Improvements in the effectiveness of contemporary neurorehabilitation emphasize the need for a shift from a specific approach to intervention to an eclectic approach to intervention. The novel strategies of brain-computer interfaces' and neuroprostheses' application in an eclectic approach to intervention may be regarded as leading the way in clinical practice development. There is a limited amount of evidence both in the areas of theoretical principles and clinical applications, but it seems the application of various rehabilitation methods and techniques may effectively support the outcomes of the BCI's and NP's use. The author aims investigates the extent to which the available opportunities are being exploited, including current and potential future applications of neuroprostheses within an eclectic approach to intervention in neurorehabilitation.

scholarly journals Towards Noninvasive Hybrid Brain–Computer Interfaces: Framework, Practice, Clinical Application, and Beyond

2015 ◽  
Vol 103 (6) ◽  
pp. 926-943 ◽  
Author(s):  
Gernot Muller-Putz ◽  
Robert Leeb ◽  
Michael Tangermann ◽  
Johannes Hohne ◽  
Andrea Kubler ◽  
...  
Keyword(s):  
Clinical Application ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

Optimizing the Usability of Brain-Computer Interfaces

2018 ◽  
Vol 30 (5) ◽  
pp. 1323-1358 ◽  
Author(s):  
Yin Zhang ◽  
Steve M. Chase
Keyword(s):  
Neural Activity ◽  
Skill Learning ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
Biomimetic Interfaces ◽  
Extended Practice ◽  
Definition Of ◽  
Open Question ◽  
The Brain

Brain-computer interfaces are in the process of moving from the laboratory to the clinic. These devices act by reading neural activity and using it to directly control a device, such as a cursor on a computer screen. An open question in the field is how to map neural activity to device movement in order to achieve the most proficient control. This question is complicated by the fact that learning, especially the long-term skill learning that accompanies weeks of practice, can allow subjects to improve performance over time. Typical approaches to this problem attempt to maximize the biomimetic properties of the device in order to limit the need for extensive training. However, it is unclear if this approach would ultimately be superior to performance that might be achieved with a nonbiomimetic device once the subject has engaged in extended practice and learned how to use it. Here we approach this problem using ideas from optimal control theory. Under the assumption that the brain acts as an optimal controller, we present a formal definition of the usability of a device and show that the optimal postlearning mapping can be written as the solution of a constrained optimization problem. We then derive the optimal mappings for particular cases common to most brain-computer interfaces. Our results suggest that the common approach of creating biomimetic interfaces may not be optimal when learning is taken into account. More broadly, our method provides a blueprint for optimal device design in general control-theoretic contexts.

Central inhibitory dysfunctions in neuropathic pain: What is the relationship between basic science and clinical practice?

1997 ◽  
Vol 20 (3) ◽  
pp. 465-465
Author(s):  
Philip J. Siddall
Keyword(s):  
Nervous System ◽  
Neuropathic Pain ◽  
Clinical Practice ◽  
Clinical Application ◽  
Basic Science ◽  
Aminobutyric Acid ◽  
Inhibitory Mechanisms ◽  
Clinical Conditions ◽  
Relationship Of ◽  
The Relationship

The possible dysfunction of γ aminobutyric acid (GABA) and opioid inhibitory mechanisms following central and peripheral nervous system injury is an important and potentially useful finding. However, effective clinical application must take into account the specific characteristics of the models used in the studies and the relationship of these models to specific clinical conditions. [dickenson; wiesenfeld-hallin et al.]

scholarly journals Generative adversarial neural networks maintain decoder accuracy during signal disruption in simulated long-term recordings from brain computer interfaces

2021 ◽  
Author(s):  
Thomas Stephens ◽  
Jon Cafaro ◽  
Ryan MacRae ◽  
Stephen B Simons
Keyword(s):  
Neural Networks ◽  
Neural Interfaces ◽  
Time Signal ◽  
Signal Recovery ◽  
Brain Computer Interfaces ◽  
Electrode Arrays ◽  
Generative Adversarial Network ◽  
Computer Interfaces ◽  
Over Time

Chronically implanted brain-computer interfaces (BCIs) provide amazing opportunities to those living with disability and for the treatment of chronic disorders of the nervous system. However, this potential has yet to be fully realized in part due to the lack of stability in measured signals over time. Signal disruption stems from multiple sources including mechanical failure of the interface, changes in neuron health, and glial encapsulation of the electrodes that alter the impedance. In this study we present an algorithmic solution to the problem of long-term signal disruption in chronically implanted neural interfaces. Our approach utilizes a generative adversarial network (GAN), based on the original Unsupervised Image to Image Translation (UNIT) algorithm, which learns how to recover degraded signals back to their analogous non-disrupted (clean) exemplars measured at the time of implant. We demonstrate that this approach can reliably recover simulated signals in two types of commonly used neural interfaces: multi-electrode arrays (MEA), and electrocorticography (ECoG). To test the accuracy of signal recovery we employ a common BCI paradigm wherein a classification algorithm (neural decoder) is trained on the starting (non-disrupted) set of signals. Performance of the decoder demonstrates expected failure over time as the signal disruption accumulates. In simulated MEA experiments, our approach recovers decoder accuracy to >90% when as many as 13/ 32 channels are lost, or as many as 28/32 channels have their neural responses altered. In simulated ECoG experiments, our approach shows stabilization of the neural decoder indefinitely with decoder accuracies >95% over simulated lifetimes of over 1 year. Our results suggest that these types of neural networks can provide a useful tool to improve the long-term utility of chronically implanted neural interfaces.

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