Review Article | Published:

Closed-loop brain training: the science of neurofeedback

Nature Reviews Neuroscience volume 18, pages 86100 (2017) | Download Citation


Neurofeedback is a psychophysiological procedure in which online feedback of neural activation is provided to the participant for the purpose of self-regulation. Learning control over specific neural substrates has been shown to change specific behaviours. As a progenitor of brain–machine interfaces, neurofeedback has provided a novel way to investigate brain function and neuroplasticity. In this Review, we examine the mechanisms underlying neurofeedback, which have started to be uncovered. We also discuss how neurofeedback is being used in novel experimental and clinical paradigms from a multidisciplinary perspective, encompassing neuroscientific, neuroengineering and learning-science viewpoints.

Key points

  • Neurofeedback is a type of biofeedback in which neural activity is measured and presented through one or more sensory channels to the participant in real time to facilitate self-regulation of the putative neural substrates that underlie a particular behaviour or pathology

  • Animal and human brain self-regulation has been demonstrated using various invasive and non-invasive recording methods and with different features of the brain signals, such as frequency spectra, functional connectivity or spatiotemporal patterns of brain activity

  • Neurofeedback provides the possibility of endogenously manipulating brain activity as an independent variable, making it a powerful neuroscientific tool

  • Neurofeedback training results in specific neural changes relevant to the trained brain circuit and the associated behavioural changes. These changes have been shown to last anywhere from hours to months after training and to correlate with changes in grey and white matter structure

  • The underlying neural circuitry relating to the process of brain self-regulation is becoming clearer. Accumulating evidence suggests the involvement of the thalamus and the dorsolateral prefrontal, posterior parietal and occipital cortices in neurofeedback control, and the dorsal and ventral striatum, anterior cingulate cortex and anterior insula in neurofeedback reward processing

  • Psychological factors, such as the differential influence of feedback, reward and experimental instructions, and other factors, such as sense of agency and locus of control, are now being investigated for their effects on neurofeedback

  • The demonstration of robust clinical effects remains a major hurdle in neurofeedback research. The results of randomized controlled trials in attention deficit and hyperactivity disorder and stroke rehabilitation have been mixed, and have been affected by differences in study design, difficulty of identifying responders and the scarcity of homogenous patient populations

  • Future neurofeedback research will probably clarify the psychological and neural mechanisms that may help to address issues in clinical translation

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The authors thank S. Ruiz and D. Schnyer for their valuable help in reviewing the manuscript before submission. R.S. is supported by the Comisión Nacional de Investigación Científica neurofeedback Tecnológica de Chile (Conicyt) through Fondo Nacional de Desarrollo Científico neurofeedback Tecnológico, Fondecyt (project number 11121153); Vicerrectoría de Investigación de la Pontificia Universidad Católica de Chile (Proyecto de Investigación Interdisciplina number15/2013); Institute for Medical and Biological Engineering and Department of Psychiatry, Pontificia Universidad Católica de Chile; the Medical Faculty of the University of Tübingen through the Fortüne funding (project number 2114-1-0); the Singapore–Bäden–Württemberg Life Sciences grant; the ERA-Net (European Research Area)–New INDIGO project funded by the Bundesministerium für Bildung und Forschung (BMBF) (project number 01DQ13004). N.W. is supported by the BRAINTRAIN European research network (Collaborative Project, grant agreement number 602186); the European Research Council (ERC) (grant agreement number 616905); and a Centre Grant by the Wellcome Trust (0915/Z/10/Z). F.S. is supported by the Swiss National Science Foundation (BSSG10_155915). L.S. is supported by the US National Institutes of Health (NIH) (K23DA032612); the Norman E. Zinberg Fellowship in Addiction Psychiatry at Harvard Medical School; the Charles A. King Trust; the McGovern Institute Neurotechnology Program; and private funds to the Massachusetts General Hospital Department of Psychiatry. N.B. is supported by Deutsche Forschungsgemeinschaft (DFG); EU Project LUMINOUS; BMBF, MOTOR-BIC und EMOIO; Eva und Horst Köhler Stiftung; Baden–Württemberg-Stiftung; and EU Project BRAINTRAIN. J.S. is supported by NIH 5K12HD073945-02.

Author information


  1. Institute for Biological and Medical Engineering, Department of Psychiatry, and Section of Neuroscience, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860 Hernán Briones, piso 2, Macul 782–0436, Santiago, Chile.

    • Ranganatha Sitaram
    •  & Mohit Rana
  2. Neurology and Imaging of Cognition Lab, University of Geneva, 1 rue Michel-Servet, 1211 Geneva, Switzerland.

    • Tomas Ros
  3. National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, 6707 Democracy Boulevard, Bethesda, Maryland 20892, USA.

    • Luke Stoeckel
  4. Affidea Centre Diagnostique Radiologique de Carouge CDRC, Clos de la Fonderie 1, 1277 Carouge, Switzerland.

    • Sven Haller
  5. Psychiatric University Hospital, University of Zürich, Lenggstrasse 31, 8032 Zürich, Switzerland.

    • Frank Scharnowski
  6. Department of Psychology, Imaging Research Center, University of Texas at Austin, 108 E Dean Keeton Street, Austin, Texas 78712, USA.

    • Jarrod Lewis-Peacock
  7. Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstrasse 1, 04103 Leipzig, Germany.

    • Nikolaus Weiskopf
  8. Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK.

    • Nikolaus Weiskopf
  9. Defitech Chair in Brain-Machine Interface, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Chemin de Mines 9, 1202 Geneva, Switzerland.

    • Maria Laura Blefari
  10. Department of Mechanical Engineering, University of Texas at Austin, 204 E Dean Keeton Street, Austin, Texas 78712, USA.

    • Ethan Oblak
    •  & James Sulzer
  11. Wyss Center for Bio and Neuroengeneering, Chenin de Mines 9, 1202 Geneva, Switzerland.

    • Niels Birbaumer


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Competing interests

N.W. is supported by the Wellcome Trust Centre for Neuroimaging and has an institutional research agreement with and receives support from Siemens Healthcare. The remaining authors have no competing interests.

Corresponding authors

Correspondence to Ranganatha Sitaram or James Sulzer.



It provides an explicit indicator of some physiological process, such as heartbeat or brain activation, so that an individual can attempt to regulate that activation or guide behaviour.

Brain–machine interfaces

(BMIs). Brain–machine interfaces, sometimes called direct neural or brain–computer interfaces, are direct communication pathways between the brain and external devices.


Simultaneous oscillations of membrane potentials in a network of neurons that are connected with electrical synapses.


Biological features (physical, physiological or behavioural) that act as robust predictors of one or more experimental or clinical outcomes.


A measure of how stable the frequency and/or phase relationship is between two neural sites; it reflects the amount of information that is shared between two sensors or channels.

Multivariate pattern analyses

(MVPAs). These are statistical and mathematical approaches for finding regularities and patterns in the data.

Adaptive neurofeedback

Previously, and perhaps imprecisely, referred to as 'closed-loop', adaptive neurofeedback changes an experimental task in real time on the basis of neural activity.

Fractional anisotropy

A property of white matter pathways of the brain that relates to the diffusion of water molecules along axonal pathways and is measured by diffusion tensor imaging; it is represented by a value ranging from 0, indicating no specific directionality, to 1, indicating one prominent directionality.

Homeostatic plasticity

The capacity of neurons to regulate their own excitability relative to network activity; it is observed in neurofeedback as an opposite and paradoxical change in brain activity after the training.

Operant conditioning

A process by which an organism learns a new association between two paired stimuli: a neutral stimulus and one that already evoked a reflexive response.

Locus of control

A psychological construct that determines the subjective feeling of being in control.

Sense of agency

The feeling that the individual causes the change.

Slow cortical potentials

(SCPs). These are slow event-related direct-current shifts that can be detected on the electroencephalogram. Slow cortical potential shifts in the electrical negative direction reflect the depolarization of large cortical cell assemblies, reducing their excitation threshold.

Fugl-Meyer scores

Performance-based impairment index for assessing motor functioning, balance, sensation and joint functioning in patients with post-stroke hemiplegia.

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