Neurofeedback and Brain–Computer Interfaces

and, Sarah Wyckoff; Birbaumer, Niels

doi:10.1002/9781118453940.ch15

Cited by 13 publications

(6 citation statements)

References 110 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A headache detection and management system can notify migraine patients' imminent migraine headaches days in advance while offering a treatment in neurofeedback form. Neurofeedbackbased BCIs could also be developed to assist the treatment of people with addiction, obesity, autism, and asthma [255]. New EEG paradigms can also be developed to facilitate cognitive control [256] and interaction with the environment [154,155].…”

Section: Neurofeedback and The Future Paradigmsmentioning

confidence: 99%

A comprehensive review of EEG-based brain–computer interface paradigms

et al. 2019

View full text Add to dashboard Cite

Advances in brain science and computer technology in the past decade have led to exciting developments in brain–computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.

show abstract

Section: Neurofeedback and The Future Paradigmsmentioning

confidence: 99%

A comprehensive review of EEG-based brain–computer interface paradigms

et al. 2019

View full text Add to dashboard Cite

show abstract

“…As NF, brain computer interfaces (BCI) also rely on a closed-loop that exploits brain activity in real-time. Traditional BCIs, so called assistive BCIs, are intended for the purpose of control and communication of an external object, while NF is intended to allow an individual to acquire control over him/herself (Wyckoff and Birbaumer, 2014 ; Perronnet et al, 2016 ). This distinction has tended to fade since the development of restorative/rehabilitative BCIs which target brain rehabilitation applications like NF (Soekadar et al, 2011 ; Chaudhary et al, 2016 ; Perronnet et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

Perronnet

Lécuyer

Mano

et al. 2017

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Neurofeedback is a promising tool for brain rehabilitation and peak performance training. Neurofeedback approaches usually rely on a single brain imaging modality such as EEG or fMRI. Combining these modalities for neurofeedback training could allow to provide richer information to the subject and could thus enable him/her to achieve faster and more specific self-regulation. Yet unimodal and multimodal neurofeedback have never been compared before. In the present work, we introduce a simultaneous EEG-fMRI experimental protocol in which participants performed a motor-imagery task in unimodal and bimodal NF conditions. With this protocol we were able to compare for the first time the effects of unimodal EEG-neurofeedback and fMRI-neurofeedback versus bimodal EEG-fMRI-neurofeedback by looking both at EEG and fMRI activations. We also propose a new feedback metaphor for bimodal EEG-fMRI-neurofeedback that integrates both EEG and fMRI signal in a single bi-dimensional feedback (a ball moving in 2D). Such a feedback is intended to relieve the cognitive load of the subject by presenting the bimodal neurofeedback task as a single regulation task instead of two. Additionally, this integrated feedback metaphor gives flexibility on defining a bimodal neurofeedback target. Participants were able to regulate activity in their motor regions in all NF conditions. Moreover, motor activations as revealed by offline fMRI analysis were stronger during EEG-fMRI-neurofeedback than during EEG-neurofeedback. This result suggests that EEG-fMRI-neurofeedback could be more specific or more engaging than EEG-neurofeedback. Our results also suggest that during EEG-fMRI-neurofeedback, participants tended to regulate more the modality that was harder to control. Taken together our results shed first light on the specific mechanisms of bimodal EEG-fMRI-neurofeedback and on its added-value as compared to unimodal EEG-neurofeedback and fMRI-neurofeedback.

show abstract

“…Restorative brain-computer and brain-machine interfaces (BCI/BMI)—emerging rehabilitation technologies for neurofeedback training—seek to reduce disease-specific symptoms in a variety of brain disorders (Wyckoff and Birbaumer, 2014 ). Unlike classical assistive BCIs, whose goal is to replace lost functions by controlling external devices, the main focus of these restorative approaches is to provide contingent feedback of specific neuronal states, thereby selectively inducing use-dependent neuroplasticity to normalize pathological brain activity and achieve behavioral gains (Daly and Wolpaw, 2008 ; Birbaumer et al, 2009 ).…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation

Bauer

Gharabaghi²

2015

Front. Neurosci.

View full text Add to dashboard Cite

Restorative brain-computer interfaces (BCI) are increasingly used to provide feedback of neuronal states in a bid to normalize pathological brain activity and achieve behavioral gains. However, patients and healthy subjects alike often show a large variability, or even inability, of brain self-regulation for BCI control, known as BCI illiteracy. Although current co-adaptive algorithms are powerful for assistive BCIs, their inherent class switching clashes with the operant conditioning goal of restorative BCIs. Moreover, due to the treatment rationale, the classifier of restorative BCIs usually has a constrained feature space, thus limiting the possibility of classifier adaptation. In this context, we applied a Bayesian model of neurofeedback and reinforcement learning for different threshold selection strategies to study the impact of threshold adaptation of a linear classifier on optimizing restorative BCIs. For each feedback iteration, we first determined the thresholds that result in minimal action entropy and maximal instructional efficiency. We then used the resulting vector for the simulation of continuous threshold adaptation. We could thus show that threshold adaptation can improve reinforcement learning, particularly in cases of BCI illiteracy. Finally, on the basis of information-theory, we provided an explanation for the achieved benefits of adaptive threshold setting.

show abstract

Neurofeedback and Brain–Computer Interfaces

Cited by 13 publications

References 110 publications

A comprehensive review of EEG-based brain–computer interface paradigms

A comprehensive review of EEG-based brain–computer interface paradigms

Unimodal Versus Bimodal EEG-fMRI Neurofeedback of a Motor Imagery Task

Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation

Contact Info

Product

Resources

About