Signal quality and Bayesian signal processing in neurofeedback based on real-time fMRI

Koush, Yury; Zvyagintsev, Mikhail; Dyck, Miriam; Mathiak, Krystyna A.; Mathiak, Klaus

doi:10.1016/j.neuroimage.2011.07.076

Cited by 54 publications

(63 citation statements)

References 47 publications

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“…The current findings hence suggest that localizer performance affects different aspects of self-regulation performance in different ways, with the optimal localizer performance depending very much on study objectives. Future studies might also consider re-adjusting the MaxPSC online for up-regulation tasks [47,48], in order to maximize task coherence between participants and sessions. Furthermore, attention should also be paid to verifying that variations in MaxPSC are not confounded with other factors of the experimental design.…”

Section: Discussionmentioning

confidence: 99%

Real-time fMRI-based self-regulation of brain activation across different visual feedback presentations

Krause

Benjamins

Lührs

et al. 2017

Brain-Computer Interfaces

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The current study is a first exploration of real-time self-regulation of functional magnetic resonance imaging (fMRI) activation based on several different visual neurofeedback presentations. Six healthy participants were engaged in self-regulation of regional fMRI activation in the posterior parietal cortex (PPC), by performing a mental calculation task. In different MR sessions, feedback was presented in the form of either a thermometer display (in vertical orientation), a circle display (increasing or decreasing in physical size), or a numbers display (Arabic digits). While self-regulation levels did differ between individuals, all six participants were able to significantly up-regulate their PPC activation with all three neurofeedback presentations. In addition to a successful general up-regulation, five out of six participants were furthermore capable of gradual self-regulation to multiple intensity levels. Taken together, the current study is a proof-of-concept demonstration of the feasibility of using multiple visual feedback presentations during gradual self-regulation of regional fMRI activation. Implications for future neurofeedback research and applications are discussed.

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Section: Discussionmentioning

confidence: 99%

Real-time fMRI-based self-regulation of brain activation across different visual feedback presentations

Krause

Benjamins

Lührs

et al. 2017

Brain-Computer Interfaces

View full text Add to dashboard Cite

show abstract

“…Tools exist to account for physiological artifacts in post-hoc analysis such as RETROICOR (Glover et al, 2000; Kasper et al, 2009), but real-time versions have yet to be published. Recent developments in signal processing in rtfMRI can further improve the robustness against such unspecific effects and noise (Hinds et al, 2011; Koush et al, 2012). One method of online physiological noise correction is to show the differential BOLD response from two different regions (Caria et al, 2007).…”

Section: Considerations In Study Designmentioning

confidence: 99%

Real-time fMRI neurofeedback: Progress and challenges

et al. 2013

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In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook

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“…The random noise can be removed by using Gaussian smoothing or temporal averaging; the scanner drift by linear trend removal, exponential moving average (Roberts, 2000; Cui et al, 2010; Koush et al, 2012), high-pass filtering, correlation analysis or generalized linear model (GLM) analysis. Global and local physiological fluctuations can also be removed by subtracting background activity, temporal filtering, or again GLM analysis with confound predictors.…”

Section: General Description Of a Hybrid Eeg-fmri Platform For Bimmentioning

confidence: 99%

How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI

et al. 2017

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Multimodal neurofeedback estimates brain activity using information acquired with more than one neurosignal measurement technology. In this paper we describe how to set up and use a hybrid platform based on simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), then we illustrate how to use it for conducting bimodal neurofeedback experiments. The paper is intended for those willing to build a multimodal neurofeedback system, to guide them through the different steps of the design, setup, and experimental applications, and help them choose a suitable hardware and software configuration. Furthermore, it reports practical information from bimodal neurofeedback experiments conducted in our lab. The platform presented here has a modular parallel processing architecture that promotes real-time signal processing performance and simple future addition and/or replacement of processing modules. Various unimodal and bimodal neurofeedback experiments conducted in our lab showed high performance and accuracy. Currently, the platform is able to provide neurofeedback based on electroencephalography and functional magnetic resonance imaging, but the architecture and the working principles described here are valid for any other combination of two or more real-time brain activity measurement technologies.

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Signal quality and Bayesian signal processing in neurofeedback based on real-time fMRI

Cited by 54 publications

References 47 publications

Real-time fMRI-based self-regulation of brain activation across different visual feedback presentations

Real-time fMRI-based self-regulation of brain activation across different visual feedback presentations

Real-time fMRI neurofeedback: Progress and challenges

How to Build a Hybrid Neurofeedback Platform Combining EEG and fMRI

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