A simulation-based approach to improve decoded neurofeedback performance

Oblak, Ethan; Sulzer, James; Lewis-Peacock, Jarrod A.

doi:10.1016/j.neuroimage.2019.03.062

Cited by 20 publications

(30 citation statements)

References 42 publications

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“…Hyperalignment achieves this by taking advantage of shared high-dimensional patterns in representational content between subjects. Another study showed that offline simulations can be used to estimate optimal parameters for experiment timing and real-time fMRI preprocessing, which can lead to greater decoding accuracy and neurofeedback performance (Oblak, Sulzer, and Lewis-Peacock 2019) . Similar approaches should be applied going forward to optimize the efficacy of DecNef tasks, thereby giving us greater power to investigate the relationship between confidence and consciousness.…”

Section: The Effect Of Neurofeedback On Color Discriminationmentioning

confidence: 99%

Multivoxel patterns for perceptual confidence are associated with false color detection

Knotts

Cortese

Taschereau-Dumouchel

et al. 2019

Preprint

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While it has been proposed that metacognition and conscious perception are related, the mechanistic relationship between the two is unclear. To address this question, we combined decoded neurofeedback (DecNef) in functional magnetic resonance imaging (fMRI) with concurrent psychophysics. Participants were rewarded for activating multivoxel patterns for color discrimination confidence while they detected color in mostly achromatic stimuli. We found that occurences of voxel patterns for high color discrimination confidence were associated with false alarms in the concurrent color detection task, suggesting a link between discrimination confidence and consciousness.

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Section: The Effect Of Neurofeedback On Color Discriminationmentioning

confidence: 99%

Multivoxel patterns for perceptual confidence are associated with false color detection

Knotts

Cortese

Taschereau-Dumouchel

et al. 2019

Preprint

View full text Add to dashboard Cite

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“…These results show that while motor 165 repetition may have caused changes in finger behavior, there is no evidence that increasing 166 pattern overlap through neurofeedback was able to increase confusion between fingers in the 167 motor or sensory domains. data was able to predict participants' abilities in a subsequent decoded neurofeedback task 192 10 (Oblak et al, 2019). By examining baseline variability of patterns, future studies may be able to 193 determine the feasibility of controlling specific neural patterns.…”

Section: Differential Modulation Of Finger Preferencementioning

confidence: 97%

Differential neural plasticity of individual fingers revealed by fMRI neurofeedback

Oblak

Lewis-Peacock

Sulzer

2020

Preprint

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Previous work has shown that fMRI activity patterns associated with individual fingers can be 1 shifted by temporary impairment of the hand (e.g. by gluing two fingers together for 24 hours). 2 Here, we investigated whether these neural activity patterns could be modulated endogenously 3 and whether any behavioral changes result from modulation of these patterns. We used decoded 4 neurofeedback in healthy individuals to encourage participants to shift the neural activity pattern in 5 sensorimotor cortex of the middle finger towards the index finger, and the ring finger towards the 6 little finger. We first mapped out the neural activity patterns for all fingers of the right hand in an 7 fMRI pattern localizer session. Then, in three subsequent neurofeedback sessions, participants 8 were rewarded after middle/ring finger presses according to their activity pattern overlap during 9 each trial. A force-sensitive keyboard was used to ensure that participants were not altering their 10 physical finger coordination patterns. We found evidence that participants could learn to shift 11 the activity pattern of the 4th digit (ring finger) but not of the 3rd digit (middle finger). Increased 12 variability of these activity patterns during the localizer session was associated with the ability 13 of participants to modulate them using neurofeedback. Participants also showed an increased 14 preference for the ring finger but not for the middle finger in a post-neurofeedback motor task. Our 15 results show that neural activity and behaviors associated with the ring finger are more readily 16 modulated than those associated with the middle finger. These results have broader implications 17 for rehabilitation of individual finger movements, which may be limited or enhanced by individual 18 finger plasticity after neurological injury. 19 1

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“…in terms of visual and semantic processing: [64,65,66]). MVPA methods have been used with success in fMRI neurofeedback studies [60,67,68,69]. 2.…”

Section: Neurofeedback System Componentsmentioning

confidence: 99%

A framework for closed-loop neurofeedback for real-time EEG decoding

Tuckute

Kjaer

et al. 2019

Preprint

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Neurofeedback based on real-time brain imaging allows for targeted training of brain activity with demonstrated clinical applications. A rapid technical development of electroencephalography (EEG)-based systems and an increasing interest in cognitive training has lead to a call for accessible and adaptable software frameworks. Here, we present and outline the core components of a novel open-source neurofeedback framework based on scalp EEG signals for real-time neuroimaging, state classification and closed-loop feedback.The software framework includes real-time signal preprocessing, adaptive artifact rejection, and cognitive state classification from scalp EEG. The framework is implemented using exclusively Python source code to allow for diverse functionality, high modularity, and easy extendibility of software development depending on the experimenter's needs.As a proof of concept, we demonstrate the functionality of our new software framework by implementing an attention training paradigm using a consumer-grade, dry-electrode EEG system. Twenty-two participants were trained on a single neurofeedback session with behavioral pre-and post-training sessions within three consecutive days. We demonstrate a mean decoding error rate of 34.3% (chance=50%) of subjective attentional states. Hence, cognitive states were decoded in real-time by continuously updating classification models on recently recorded EEG data without the need for any EEG recordings prior to the neurofeedback session.The proposed software framework allows a wide range of users to actively engage in the development of novel neurofeedback tools to accelerate improvements in neurofeedback as a translational and therapeutic tool.

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A simulation-based approach to improve decoded neurofeedback performance

Cited by 20 publications

References 42 publications

Multivoxel patterns for perceptual confidence are associated with false color detection

Multivoxel patterns for perceptual confidence are associated with false color detection

Differential neural plasticity of individual fingers revealed by fMRI neurofeedback

A framework for closed-loop neurofeedback for real-time EEG decoding

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