Closed-loop brain training: the science of neurofeedback

R Sitaram, T Ros, L Stoeckel, S Haller… - Nature Reviews …, 2017 - nature.com
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 …

EEG-based brain–computer interfaces

DJ McFarland, JR Wolpaw - current opinion in Biomedical Engineering, 2017 - Elsevier
Abstract Brain–Computer Interfaces (BCIs) are real-time computer-based systems that
translate brain signals into useful commands. To date most applications have been …

[PDF][PDF] Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist)

T Ros, S Enriquez-Geppert, V Zotev, KD Young… - 2020 - academic.oup.com
Consensus on the reporting and experimental design of clinical and cognitive-behavioural
neurofeedback studies (CRED-nf checklis Page 1 UPDATE Consensus on the reporting and …

Integrated use of biofeedback and neurofeedback techniques in treating pathological conditions and improving performance: A narrative review

B Tosti, S Corrado, S Mancone, T Di Libero… - Frontiers in …, 2024 - frontiersin.org
In recent years, the scientific community has begun tо explore the efficacy оf an integrated
neurofeedback+ biofeedback approach іn various conditions, both pathological and non …

Can we predict who will respond to neurofeedback? A review of the inefficacy problem and existing predictors for successful EEG neurofeedback learning

O Alkoby, A Abu-Rmileh, O Shriki, D Todder - Neuroscience, 2018 - Elsevier
Despite the success of neurofeedback treatment in many cases, the variability in the efficacy
of the treatment is high, and some studies report that a significant proportion of subjects …

The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users

S Perdikis, L Tonin, S Saeedi, C Schneider… - PLoS …, 2018 - journals.plos.org
This work aims at corroborating the importance and efficacy of mutual learning in motor
imagery (MI) brain–computer interface (BCI) by leveraging the insights obtained through our …

A review of user training methods in brain computer interfaces based on mental tasks

A Roc, L Pillette, J Mladenovic… - Journal of Neural …, 2021 - iopscience.iop.org
Mental-tasks based brain–computer interfaces (MT-BCIs) allow their users to interact with an
external device solely by using brain signals produced through mental tasks. While MT-BCIs …

Neurofeedback through the lens of reinforcement learning

N Lubianiker, C Paret, P Dayan, T Hendler - Trends in Neurosciences, 2022 - cell.com
Despite decades of experimental and clinical practice, the neuropsychological mechanisms
underlying neurofeedback (NF) training remain obscure. NF is a unique form of …

A large EEG database with users' profile information for motor imagery brain-computer interface research

P Dreyer, A Roc, L Pillette, S Rimbert, F Lotte - Scientific Data, 2023 - nature.com
We present and share a large database containing electroencephalographic signals from 87
human participants, collected during a single day of brain-computer interface (BCI) …

Current progress in real-time functional magnetic resonance-based neurofeedback: methodological challenges and achievements

C Paret, N Goldway, C Zich, JN Keynan, T Hendler… - NeuroImage, 2019 - Elsevier
Neurofeedback (NF) is a research and clinical technique, characterized by live
demonstration of brain activation to the subject. The technique has become increasingly …