ArticleScientific reports2018
Machine learning provides novel neurophysiological features that predict performance to inhibit automated responses.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Theta/Beta Ratio Neurofeedback Effects on Resting and Task-Related Theta Activity in Children with ADHD.Applied psychophysiology and biofeedback · 2025Trial
- Time Overestimation Devalues Future Rewards: Electroencephalogram Evidence from Intertemporal Choice.Brain sciences · 2026Article
- Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models.Neuroinformatics · 2024Article
- Adolescents rats engage the orbitofrontal-striatal pathway differently than adults during impulsive actions.Scientific reports · 2024Article
- Real-time cortical dynamics during motor inhibition.Scientific reports · 2024Article
- Alpha and theta band activity share information relevant to proactive and reactive control during conflict-modulated response inhibition.Human brain mapping · 2023Article
- Article
- On the relative importance of attention and response selection processes for multi-component behavior - Evidence from EEG-based deep learning.Neuroimage. Reports · 2022Article
- Training a machine learning classifier to identify ADHD based on real-world clinical data from medical records.Scientific reports · 2022Article
- Resting-state theta activity is linked to information content-specific coding levels during response inhibition.Scientific reports · 2022Article
- Superior frontal regions reflect the dynamics of task engagement and theta band-related control processes in time-on task effects.Scientific reports · 2022Article
- The interplay of resting and inhibitory control-related theta-band activity depends on age.Human brain mapping · 2021Article
- Article
- Facial expressions contribute more than body movements to conversational outcomes in avatar-mediated virtual environments.Scientific reports · 2020Article
- Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control.Communications biology · 2020Article
- On the relevance of EEG resting theta activity for the neurophysiological dynamics underlying motor inhibitory control.Human brain mapping · 2019Article
- Article
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5 authors.
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Abstract
Neurophysiological features like event-related potentials (ERPs) have long been used to identify different cognitive sub-processes that may contribute to task performance. It has however remained unclear whether "classical" ERPs are truly the best reflection or even causal to observable variations in behavior. Here, we used a data-driven strategy to extract features from neurophysiological data of n = 240 healthy young individuals who performed a Go/Nogo task and used machine learning methods in combination with source localization to identify the best predictors of inter-individual performance variations. Both Nogo-N2 and Nogo-P3 yielded predictions close to chance level, but a feature in between those two processes, associated with motor cortex activity (BA4), predicted group membership with up to ~68%. We further found two Nogo-associated features in the theta and alpha bands, that predicted behavioral performance with up to ~78%. Notably, the theta band feature contributed most to the prediction and occurred at the same time as the predictive ERP feature. Our approach provides a rigorous test for established neurophysiological correlates of response inhibition and suggests that other processes, which occur in between the Nogo-N2 and P3, might be of equal, if not even greater, importance.
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