Various behavioural tasks measure response inhibition encompassing the ability to cancel unwanted actions, evaluated via stop signal reaction time (SSRT). It is unclear whether SSRT is an unchangeable inherent measure of inhibitory network integrity or whether it can improve with repetition. The current study explored if and how SSRT changed over two sessions for the Anticipatory Response Inhibition Task (ARIT), and how this compared with the Stop Signal Task (SST). Forty-four participants repeated the ARIT and SST over two sessions. SSRT and its constituent measures (Go trial reaction time, stop signal delay) were calculated. SSRT reflecting non-selective response inhibition was consistent between sessions in the ARIT and SST (both p > 0.293). Reaction time and stop signal delay also remained stable across sessions in the ARIT (all p > 0.063), whereas in the SST, reaction time (p = 0.013) and stop signal delay (p = 0.009) increased. SSRT reflecting behaviourally selective stopping on the ARIT improved (p < 0.001) over two sessions, which was underpinned by changes to reaction time (p < 0.001) and stop signal delay (p < 0.001). Overall, the maximal efficiency of non-selective inhibition remained stable across two sessions in the ARIT. Results of the SST confirmed that non-selective inhibition can, however, be affected by more than inhibitory network integrity. Behaviourally selective stopping on the ARIT changed across sessions, suggesting the sequential neural process captured by the SSRT occurred more quickly in session two. These findings have implications for future studies that necessitate behavioural measures over multiple sessions. 

/pdf/exploring-stop-signal-reaction-time-over-two-sessions-of-the-1opaij17.pdf

Exploring stop signal reaction time over two sessions of the anticipatory response inhibition task

Abstract Neural oscillations, or brain rhythms, fluctuate in a manner reflecting ongoing behavior. Whether these fluctuations are instrumental or epiphenomenal to the behavior remains elusive. Attempts to experimentally manipulate neural oscillations exogenously using noninvasive brain stimulation have shown some promise, but difficulty with tailoring stimulation parameters to individuals has hindered progress in this field. We demonstrate here using electroencephalography (EEG) neurofeedback in a brain‐computer interface that human participants (n = 44) learned over multiple sessions across a 6‐day period to self‐regulate their Beta rhythm (13–20 Hz), either up or down, over the right inferior frontal cortex. Training to downregulate Beta was more effective than training to upregulate Beta. The modulation was evident only during neurofeedback task performance but did not lead to offline alteration of Beta rhythm characteristics at rest, nor to changes in subsequent cognitive behavior. Likewise, a control group (n = 38) who underwent training to up or downregulate the Alpha rhythm (8–12 Hz) did not exhibit behavioral changes. Although the right frontal Beta rhythm has been repeatedly implicated as a key component of the brain's inhibitory control system, the present data suggest that its manipulation offline prior to cognitive task performance does not result in behavioral change in healthy individuals. Whether this form of neurofeedback training could serve as a useful therapeutic target for disorders with dysfunctional inhibitory control as their basis remains to be tested in a context where performance is abnormally poor and neural dynamics are different.

/pdf/self-regulation-of-the-brain-s-right-frontal-beta-rhythm-2b1lqbwh.pdf

Self‐regulation of the brain's right frontal Beta rhythm using a brain‐computer interface

Attention is a cognitive process that involves focusing mental resources on specific stimuli and plays a fundamental role in perception, learning, memory, and decision-making. Neurofeedback (NF) is a useful technique for improving attention, providing real-time feedback on brain activity in the form of visual or auditory cues, and allowing users to learn to self-regulate their cognitive processes. This study compares the effectiveness of different cues in NF training for attention enhancement through a multimodal approach. We conducted neurological (Quantitative Electroencephalography), neuropsychological (Mindfulness Attention Awareness Scale-15), and behavioral (Stroop test) assessments before and after NF training on 36 healthy participants, divided into audiovisual (G1) and visual (G2) groups. Twelve NF training sessions were conducted on alternate days, each consisting of five subsessions, with pre- and post-NF baseline electroencephalographic evaluations using power spectral density. The pre-NF baseline was used for thresholding the NF session using the beta frequency band power. Two-way analysis of variance revealed a significant long-term effect of group (G1/G2) and state (before/after NF) on the behavioral and neuropsychological assessments, with G1 showing significantly higher Mindfulness Attention Awareness Scale-15 scores, higher Stroop scores, and lower Stroop reaction times for interaction effects. Moreover, unpaired t -tests to compare voxel-wise standardized low-resolution brain electromagnetic tomography images revealed higher activity of G1 in Brodmann area 40 due to NF training. Neurological assessments show that G1 had better improvement in immediate, short-, and long-term attention. The findings of this study offer a guide for the development of NF training protocols aimed at enhancing attention effectively. 

Efficacy of audiovisual neurofeedback training for attention enhancement: a multimodal approach

Assessing the Effectiveness of Audio-Visual vs. Visual Neurofeedback for Attention Enhancement: A Pilot Study with Neurological, Behavioural, and Neuropsychological Measures

Abstract Neurofeedback training (NFT) provides individuals with real-time feedback on their brain activity, enabling voluntary modulation. Interest in NFT for memory enhancement has surpassed the cumulative evidence in healthy adults, especially for modalities feasible outside of laboratory settings. To address this gap, this systematic review and meta-analysis focused on electroencephalography (EEG) and functional near- infrared spectroscopy (fNIRS), both of which are portable and widely accessible. Randomized controlled trials published between January 1990 and September 2022 were retrieved from PubMed, Scopus, Web of Science, and APA PsycInfo. Of the 44 eligible studies, 24 reported sufficient data for inclusion in the meta-analysis. Using a random-effects model, we found that NFT produced a small-to-moderate improvement in memory performance (standardized mean difference = 0.28; 95% confidence interval = 0.065–0.48; t (11.9) = 2.86; p = 0.014), with moderate heterogeneity. No publication bias was detected. Subgroup meta-analyses revealed significant effects in younger adults, for EEG-based protocols, and in tasks assessing verbal and short-term memory tasks. Meta-regression identified a positive association between daily session duration and effect size, with sessions longer than 30 minutes more likely to produce positive effects. As effects also varied by targeted brain regions and EEG frequency bands used for neurofeedback, protocol differences likely contributed to the observed variability in outcomes. Long- term follow-up is warranted to determine the persistence of NFT effects. 

Does wearable neurofeedback training improve memory aptitudes in healthy adults? ––A systematic review and meta-analysis

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Jemima Schmidt

Author Tools

Chat about Author

Papers

Self‐regulation of the brain's right frontal Beta rhythm using a brain‐computer interface