ArticleCommunications biology2020
Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control.
Article in Communications biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Mapping the Digital Mind: A Meta-Analysis of EEG Biomarkers in Cognition, Emotion, and Mental Health.Brain sciences · 2026Review
- Inferring Arm Movement Direction from EEG Signals Using Explainable Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- A deep learning-enriched framework for analyzing brain functional connectivity.Scientific reports · 2025Article
- Where do I go? Decoding temporal neural dynamics of scene processing and visuospatial memory interactions using convolutional neural networks.Journal of vision · 2025Article
- From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications.Brain sciences · 2025Review
- What does my network learn? Assessing interpretability of deep learning for EEG.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.Frontiers in neuroinformatics · 2025Article
- Investigating the role of auditory cues in modulating motor timing: insights from EEG and deep learning.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Response stopping under conflict: The integrative role of representational dynamics associated with the insular cortex.Human brain mapping · 2024Article
- Simultaneous EEG-fNIRS Data Classification Through Selective Channel Representation and Spectrogram Imaging.IEEE journal of translational engineering in health and medicine · 2024Article
- Self supervised learning based emotion recognition using physiological signals.Frontiers in human neuroscience · 2024Article
- Investigation of frequency components embedded in EEG recordings underlying neuronal mechanism of cognitive control and attentional functions.Cognitive neurodynamics · 2023Article
- Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan.Brain informatics · 2023Article
- Deep learning applied to EEG source-data reveals both ventral and dorsal visual stream involvement in holistic processing of social stimuli.Scientific reports · 2023Article
- Energy-Efficient EEG-Based Scheme for Autism Spectrum Disorder Detection Using Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Evidence for independent representational contents in inhibitory control subprocesses associated with frontoparietal cortices.Human brain mapping · 2023Article
- On the Role of Stimulus-Response Context in Inhibitory Control in Alcohol Use Disorder.Journal of clinical medicine · 2022Article
- Explainable Machine-Learning-Based Characterization of Abnormal Cortical Activities for Working Memory of Restless Legs Syndrome Patients.Sensors (Basel, Switzerland) · 2022Article
- On the relative importance of attention and response selection processes for multi-component behavior - Evidence from EEG-based deep learning.Neuroimage. Reports · 2022Article
- Categorizing objects from MEG signals using EEGNet.Cognitive neurodynamics · 2022Article
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5 authors.
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Abstract
Efficient action control is indispensable for goal-directed behaviour. Different theories have stressed the importance of either attention or response selection sub-processes for action control. Yet, it is unclear to what extent these processes can be identified in the dynamics of neurophysiological (EEG) processes at the single-trial level and be used to predict the presence of conflicts in a given moment. Applying deep learning, which was blind to cognitive theory, on single-trial EEG data allowed to predict the presence of conflict in ~95% of subjects ~33% above chance level. Neurophysiological features related to attentional and motor response selection processes in the occipital cortex and the superior frontal gyrus contributed most to prediction accuracy. Importantly, deep learning was able to identify predictive neurophysiological processes in single-trial neural dynamics. Hence, mathematical (artificial intelligence) approaches may be used to foster the validation and development of links between cognitive theory and neurophysiology of human behavior.
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