Generalization of skills between operant control and discrimination of EEG alpha

Frederick, Jon A.; Heim, Andrew S.; Dunn, Kelli N.; Powers, Cynthia D.; Klein, Casey J.

doi:10.1016/j.concog.2016.09.009

Cited by 11 publications

(16 citation statements)

References 47 publications

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“…Our conclusion is in agreement with Schurger and colleagues (2017), who found that with training participants learned to better evaluate their actions performed with EEG-based motor imagery task. They are also in line with previous EEG-based neurofeedback studies looking at self-discrimination of the alpha rhythm (Frederick, 2012;Frederick et al, 2016;Kamiya, 1962).…”

Section: Participants Improve Their Performance Predictionsupporting

confidence: 91%

“…One theory proposes that neurofeedback learning is a form of instrumental conditioning by which a neural response becomes more likely when associated with a reward (Fetz, 2007;Kamiya, 1962;Shibata et al, 2019). Another proposal suggests that feedback exposure allows control of physiological activity by increasing our awareness of internal -otherwise inaccessible-states (Brener, 1977;Brown, 1971;Frederick et al, 2016;Neumann et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Online self-evaluation of fMRI-based neurofeedback performance

Muñoz-Moldes

Tursic

Eck

2021

Preprint

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This study explores the subjective evaluation of supplementary motor area (SMA) regulation performance in a real-time functional magnetic resonance imaging neurofeedback (fMRI-NF) task. In fMRI-NF, people learn how to self-regulate their brain activity by performing mental actions to achieve a certain target level of blood-oxygen-level-dependent (BOLD) activation. This setup offers the possibility to study performance monitoring in the absence of somatosensory feedback. Here, we studied two types of self-evaluation expressed before receiving neurofeedback: performance predictions and perceived confidence in the prediction judgement. We hypothesized that throughout learning, participants would (1) improve the precision of their performance predictions about the actual changes in their BOLD response, and (2) that reported confidence would progressively increase with improved metacognitive precision. Participants completed three sessions of SMA regulation in a 7T fMRI scanner, performing a drawing motor imagery task. During each trial, they modulated their mental drawing strategy to achieve one of two different levels of target fMRI signal change. They then reported a performance prediction and their confidence in the prediction before receiving delayed BOLD-activation feedback. Results show that participants' performance predictions improved with learning throughout the three sessions, and that these improvements were not driven exclusively by their knowledge of previous performance. Confidence reports on the other hand showed no change throughout training and did not differentiate between the better and worse predictions. In addition to shedding light on mechanisms of internal monitoring during neurofeedback training, these results also point to a dissociation between self-evaluation of performance and corresponding reported confidence in the presence of feedback.

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Section: Participants Improve Their Performance Predictionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Online self-evaluation of fMRI-based neurofeedback performance

Muñoz-Moldes

Tursic

Eck

2021

Preprint

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“…Changes in the alpha frequency are observed during visual learning tasks as well as during intelligence tasks. Alpha activity increases in the frontal region and decreases in the right parietal and right temporal regions during visual learning (Frederick et al, 2016; Tóth et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities

Jawed

Amin

Malik³

et al. 2019

Front. Behav. Neurosci.

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This study analyzes the learning styles of subjects based on their electroencephalo-graphy (EEG) signals. The goal is to identify how the EEG features of a visual learner differ from those of a non-visual learner. The idea is to measure the students’ EEGs during the resting states (eyes open and eyes closed conditions) and when performing learning tasks. For this purpose, 34 healthy subjects are recruited. The subjects have no background knowledge of the animated learning content. The subjects are shown the animated learning content in a video format. The experiment consists of two sessions and each session comprises two parts: (1) Learning task: the subjects are shown the animated learning content for an 8–10 min duration. (2) Memory retrieval task The EEG signals are measured during the leaning task and memory retrieval task in two sessions. The retention time for the first session was 30 min, and 2 months for the second session. The analysis is performed for the EEG measured during the memory retrieval tasks. The study characterizes and differentiates the visual learners from the non-visual learners considering the extracted EEG features, such as the power spectral density (PSD), power spectral entropy (PSE), and discrete wavelet transform (DWT). The PSD and DWT features are analyzed. The EEG PSD and DWT features are computed for the recorded EEG in the alpha and gamma frequency bands over 128 scalp sites. The alpha and gamma frequency band for frontal, occipital, and parietal regions are analyzed as these regions are activated during learning. The extracted PSD and DWT features are then reduced to 8 and 15 optimum features using principal component analysis (PCA). The optimum features are then used as an input to the k -nearest neighbor ( k -NN) classifier using the Mahalanobis distance metric, with 10-fold cross validation and support vector machine (SVM) classifier using linear kernel, with 10-fold cross validation. The classification results showed 97% and 94% accuracies rate for the first session and 96% and 93% accuracies for the second session in the alpha and gamma bands for the visual learners and non-visual learners, respectively, for k -NN classifier for PSD features and 68% and 100% accuracies rate for first session and 100% accuracies rate for second session for DWT features using k -NN classifier for the second session in the alpha and gamma band. For PSD features 97% and 96% accuracies rate for the first session, 100% and 95% accuracies rate for second session using SVM classifier and 79% and 82% accuracy for first session and 56% and 74% accuracy for second session for DWT features using SVM classifier. The results showed that the PSDs in the alpha and gamma bands represent distinct and stable EEG signatures for visual learners and non-visual learners during the retrieval of the learned contents.

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“…Here, by providing externalized information of internal states that do not usually surpass the threshold for awareness, biofeedback is seen as enabling the identification of those subtle sensations and as serving as a "tool for self-investigation" (Zolten, 1989). It is therefore seen as form of sensory substitution, similar to how deaf person would use tactile and visual feedback to learn to speak (Frederick et al, 2016). The theory predicts that regulation is correlated with discrimination ability, that discrimination is sufficient for control, and that regulatory actions become, with training, increasingly refined to physiological subsystems.…”

Section: Awareness Theorymentioning

confidence: 99%

“…Above-chance accuracy in this case is taken as an indication that the participant is aware of his mental state (or has some relevant information) allowing him or her to indicate if alpha was low or high. Several studies have used such measures in neurofeedback, such as for slow cortical potential discrimination (Kotchoubey et al, 2002) or alpha level discrimination (Frederick et al, 2019(Frederick et al, , 2016Frederick, 2012). Beyond dichotomous choices, Schurger and colleagues (2017) used continuous ratings for evaluating mental actions: participants rated the position of a cursor that was driven by their sensorimotor activity on a 1-10 point scale, before seeing their feedback, which allows to verify whether participant's continuous guesses correlate with trial-to-trial performance.…”

Section: Objective Measuresmentioning

confidence: 99%

Delineating implicit and explicit processes in neurofeedback learning

Muñoz-Moldes¹,

Cleeremans²

2020

Preprint

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Neurofeedback allows humans to self-regulate neural activity in specific brain regions and is considered a promising tool for psychiatric interventions. Recently, methods have been developed to use neurofeedback implicitly, prompting a theoretical debate on the role of awareness in neurofeedback learning. We offer a critical review of the role of awareness in neurofeedback learning, with a special focus on recently developed neurofeedback paradigms. We detail differences in instructions and propose a fine-grained categorization of tasks based on the degree of involvement of explicit and implicit processes. Finally, we review the methods used to measure awareness in neurofeedback and propose new candidate measures. We conclude that explicit processes cannot be eschewed in most current implicit tasks that have explicit goals, and suggest ways in which awareness could be better measured in the future. Investigating awareness during learning will help understand the learning mechanisms underlying neurofeedback learning and will help shape future tasks.

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Generalization of skills between operant control and discrimination of EEG alpha

Cited by 11 publications

References 47 publications

Online self-evaluation of fMRI-based neurofeedback performance

Online self-evaluation of fMRI-based neurofeedback performance

Classification of Visual and Non-visual Learners Using Electroencephalographic Alpha and Gamma Activities

Delineating implicit and explicit processes in neurofeedback learning

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