Closed-loop brain training: the science of neurofeedback

Sitaram, Ranganatha; Ros, Tomas; Stoeckel, Luke E.; Haller, Sven; Scharnowski, Frank; Lewis-Peacock, Jarrod A.; Weiskopf, Nikolaus; Blefari, Maria Laura; Rana, Mohit; Oblak, Ethan; Birbaumer, Niels; Sulzer, James

doi:10.1038/nrn.2016.164

Cited by 989 publications

(935 citation statements)

References 214 publications

(187 reference statements)

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“…(2) Rehabilitation technologies also referred to as neurofeedback or rehabilitative BCIs,24 that aim to foster neuroplasticity through manipulation or self‐regulation of neurophysiological activity facilitating motor recovery. In the current work, we focus on BCIs as rehabilitative technology in post‐stroke motor rehabilitation.…”

Section: Introductionmentioning

confidence: 99%

“…An associated real‐time feedback system (e.g., a robotic orthosis, NMES or a virtual reality avatar) that reproduces the intended action (e.g., finger extension) allows patients to purposefully control sensorimotor oscillations 24. Similar to motor learning mechanisms, BCI‐mediated motor training is thought to involve Hebbian neuroplasticity, error‐based learning, and reward‐based learning 35…”

Section: Introductionmentioning

confidence: 99%

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Brain‐computer interfaces for post‐stroke motor rehabilitation: a meta‐analysis

Cervera

Soekadar

Ushiba

et al. 2018

Ann Clin Transl Neurol

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362

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Brain‐computer interfaces (BCIs) can provide sensory feedback of ongoing brain oscillations, enabling stroke survivors to modulate their sensorimotor rhythms purposefully. A number of recent clinical studies indicate that repeated use of such BCIs might trigger neurological recovery and hence improvement in motor function. Here, we provide a first meta‐analysis evaluating the clinical effectiveness of BCI‐based post‐stroke motor rehabilitation. Trials were identified using MEDLINE, CENTRAL, PEDro and by inspection of references in several review articles. We selected randomized controlled trials that used BCIs for post‐stroke motor rehabilitation and provided motor impairment scores before and after the intervention. A random‐effects inverse variance method was used to calculate the summary effect size. We initially identified 524 articles and, after removing duplicates, we screened titles and abstracts of 473 articles. We found 26 articles corresponding to BCI clinical trials, of these, there were nine studies that involved a total of 235 post‐stroke survivors that fulfilled the inclusion criterion (randomized controlled trials that examined motor performance as an outcome measure) for the meta‐analysis. Motor improvements, mostly quantified by the upper limb Fugl‐Meyer Assessment (FMA‐UE), exceeded the minimal clinically important difference (MCID=5.25) in six BCI studies, while such improvement was reached only in three control groups. Overall, the BCI training was associated with a standardized mean difference of 0.79 (95% CI: 0.37 to 1.20) in FMA‐UE compared to control conditions, which is in the range of medium to large summary effect size. In addition, several studies indicated BCI‐induced functional and structural neuroplasticity at a subclinical level. This suggests that BCI technology could be an effective intervention for post‐stroke upper limb rehabilitation. However, more studies with larger sample size are required to increase the reliability of these results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Brain‐computer interfaces for post‐stroke motor rehabilitation: a meta‐analysis

Cervera

Soekadar

Ushiba

et al. 2018

Ann Clin Transl Neurol

Self Cite

362

267

View full text Add to dashboard Cite

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“…Think also of the future: Algorithms reading cortical activity directly from the brain, transmitting signals from a paralyzed human's motor cortex to hand muscles and restoring motor control [5]. Or cortical activity down-regulated via "neuroHorst Urbach horst.urbach@uniklinik-freiburg.de 1 Klinik für Neuroradiologie, Universitätsklinikum Freiburg, Freiburg, Germany feedback", which may be used to suppress tinnitus [6,7] or improve speech recovery after stroke. Such advances would unimaginable without machine learning and processing of real-time high-resolution physiological data.…”

mentioning

confidence: 99%

Emerging Challenges for Neuroradiologists

Urbach

2017

Clin Neuroradiol

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“…Modern neurotechnology encompasses several types of tools and applications intended for both neurological sensing and intervention purposes. Among the sensing kind stand out such direct neuromonitoring tools as electroencephalography (EEG), used for measuring central nervous system electrical activity and to generate neurofeedback signals (Sitaram et al, 2017). Other more sophisticated brain monitoring tools that .…”

Section: Computerized Neurocognitive Interventions and The Brain Traimentioning

confidence: 99%

“…Other more sophisticated brain monitoring tools that . They can be used as stand-alone interventions, or in conjunction with electric neurofeedback to induce some neuroplastic effect at specific brain locations, with the aim of modulating particular brain functions (Sitaram et al, 2017).…”

Section: Computerized Neurocognitive Interventions and The Brain Traimentioning

confidence: 99%

A methodological framework for exploring the applicability of virtual reality in cognitive disabilities care

Betances¹,

Isabel²

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There is always going to be a reason why you can't do something, your job is toconstantly look for the reasons why you can achieve your dreams. Shannon Miller -7x Olympic Gymnast medallist AcknowledgmentsI would like to firstly express my sincere gratitude to my advisor ProfessorMaría Teresa Arredondo Waldmeyer for her continuous support of my PhD studies and related research, and for her inspiration, tenacity, and immense knowledge.In addition to my advisor, I thank Prof. María Fernanda Cabrera-Umpiérrez and Prof. Francisco J. García-Sánchez, not only for their insightful commentaries and supportive encouragement, but also for the helpful suggestions and revision recommendations which motivated me to extend the scope of my research work to wider perspectives.I also thank my former colleagues and present fellow teammates at LifeSTech for the stimulating discussions, help and advice, as well as for the interesting times we shared working and learning together at several projects during the last years.Last but not least, I thank my family and friends for supporting all my life goals and purposes, and for the patience shown while I was writing this thesis.ix AbstractHandling the care needs of people affected by Alzheimer's disease and related dementias (ADRD) represents nowadays a personal, familial and social challenge of enormous proportions, which poses an ever growing human and economic burden on the world's society in general. Information and communication technologies (ICT) have the potential to make a considerable impact to help revert this trend, especially through the use of advanced virtual reality (VR)-based computerized diagnostic and therapeutic cognitive interventions for people living with, or at risk of developing,ADRD. This doctoral thesis aims to facilitate the use of such VR-based interventions.To that end, it makes specific original contributions regarding: the categorization of VR systems particularly designed for ADRD; an analysis and assessment of the current relevance and impact of the 'brain training debate' involving commercial brain training products; an up-to-date critical description of the technical and scientific challenges faced today for the development of effective VR-based cognitive training tools; and finally, this thesis proposes a novel methodological design framework to provide guidance to researchers and developers with the design process of VR-based tools to address the care needs of people with ADRD. The framework is based on our previous critical analysis of the most recent knowledge available about VR cognitive interventions. The framework and its ensuing roadmap take into account, not only universal design principles, standard techniques and methodologies, but also addresses the specific aspects of accessibility and usability that are particular to the use VR-based platforms on patients with ADRD-associated cognitive impairments.x xi ResumenEl manejo de las necesidades de atención de las personas afectadas por la enfermedad de Alzheimer y demencias relacionadas (EADR)...

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Closed-loop brain training: the science of neurofeedback

Cited by 989 publications

References 214 publications

Brain‐computer interfaces for post‐stroke motor rehabilitation: a meta‐analysis

Brain‐computer interfaces for post‐stroke motor rehabilitation: a meta‐analysis

Emerging Challenges for Neuroradiologists

A methodological framework for exploring the applicability of virtual reality in cognitive disabilities care

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