ArticleJournal of neural engineering2019
Decoding task engagement from distributed network electrophysiology in humans.
Article in Journal of neural engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Personalized cognitive enhancement for older adults: An aging-friendly closed-loop human-machine interface framework.Ageing research reviews · 2025Review
- Neurostimulation to Improve Cognitive Flexibility.Current opinion in behavioral sciences · 2025Article
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- Neural decoding and feature selection methods for closed-loop control of avoidance behavior.Journal of neural engineering · 2024Article
- Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior.bioRxiv : the preprint server for biology · 2024Article
- Closing the loop in psychiatric deep brain stimulation: physiology, psychometrics, and plasticity.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Closed-loop enhancement and neural decoding of cognitive control in humans.Nature biomedical engineering · 2023Article
- Causally mapping human threat extinction relevant circuits with depolarizing brain stimulation methods.Neuroscience and biobehavioral reviews · 2023Review
- Modular pipeline for reconstruction and localization of implanted intracranial ECoG and sEEG electrodes.PloS one · 2023Article
- Neural mass model-based study of frontal-temporal theta oscillations in human subjects during the performance of a cognitive control task.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
- Article
- Long-term ecological assessment of intracranial electrophysiology synchronized to behavioral markers in obsessive-compulsive disorder.Nature medicine · 2021Article
- Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection.IEEE transactions on biomedical circuits and systems · 2021Review
- Predicting task performance from biomarkers of mental fatigue in global brain activity.Journal of neural engineering · 2021Article
- Deep brain stimulation for psychiatric disorders: From focal brain targets to cognitive networks.NeuroImage · 2021Review
- Patterns of Focal- and Large-Scale Synchronization in Cognitive Control and Inhibition: A Review.Frontiers in human neuroscience · 2020Review
- Prefrontal cortex and cognitive control: new insights from human electrophysiology.F1000Research · 2019Review
- Region-Level Functional and Effective Network Analysis of Human Brain During Cognitive Task Engagement.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology SocietyArticle
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9 authors.
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Abstract
objectiveHere, our objective was to develop a binary decoder to detect task engagement in humans during two distinct, conflict-based behavioral tasks. Effortful, goal-directed decision-making requires the coordinated action of multiple cognitive processes, including attention, working memory and action selection. That type of mental effort is often dysfunctional in mental disorders, e.g. when a patient attempts to overcome a depression or anxiety-driven habit but feels unable. If the onset of engagement in this type of focused mental activity could be reliably detected, decisional function might be augmented, e.g. through neurostimulation. However, there are no known algorithms for detecting task engagement with rapid time resolution. APPROACH: We defined a new network measure, fixed canonical correlation (FCCA), specifically suited for neural decoding applications. We extracted FCCA features from local field potential recordings in human volunteers to give a temporally continuous estimate of mental effort, defined by engagement in experimental conflict tasks. MAIN
resultsUsing a small number of features per participant, we accurately decoded and distinguished task engagement from other mental activities. Further, the decoder distinguished between engagement in two different conflict-based tasks within seconds of their onset. SIGNIFICANCE: These results demonstrate that network-level brain activity can detect specific types of mental efforts. This could form the basis of a responsive intervention strategy for decision-making deficits.
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