ReviewFrontiers in human neuroscience2018
Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior.
Review in Frontiers in human neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- Model based fitting of pattern reversal visually evoked potentials provides a reliable characterization of waveform components.Biomedical signal processing and control · 2025Article
- Linking brain-heart interactions to emotional arousal in immersive virtual reality.Psychophysiology · 2024Article
- Assessing the influence of latency variability on EEG classifiers - a case study of face repetition priming.Cognitive neurodynamics · 2024Article
- Weighted Coherence Analysis as a Window into the Neurophysiological Effects of Traumatic Brain Injury.Bioengineering (Basel, Switzerland) · 2024Article
- A tutorial on fitting joint models of M/EEG and behavior to understand cognition.Behavior research methods · 2024Article
- Tracking gaze position from EEG: Exploring the possibility of an EEG-based virtual eye-tracker.Brain and behavior · 2023Article
- Long-term practice of intuitive inquiry meditation modulates EEG dynamics during self-schema processing.Heliyon · 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
- Association of Maternal Smoking during Pregnancy with Neurophysiological and ADHD-Related Outcomes in School-Aged Children.International journal of environmental research and public health · 2023Article
- Self-deception: Distorted metacognitive process in ambiguous contexts.Human brain mapping · 2023Article
- On the Role of Stimulus-Response Context in Inhibitory Control in Alcohol Use Disorder.Journal of clinical medicine · 2022Article
- Preliminary Evidence of EEG Connectivity Changes during Self-Objectification of Workers.Sensors (Basel, Switzerland) · 2022Article
- A ventral stream-prefrontal cortex processing cascade enables working memory gating dynamics.Communications biology · 2022Article
- On the relative importance of attention and response selection processes for multi-component behavior - Evidence from EEG-based deep learning.Neuroimage. Reports · 2022Article
- Behavioural and neural indices of perceptual decision-making in autistic children during visual motion tasks.Scientific reports · 2022Article
- Conditional generative adversarial networks applied to EEG data can inform about the inter-relation of antagonistic behaviors on a neural level.Communications biology · 2022Article
- Toward individualized medicine in stroke-The TiMeS project: Protocol of longitudinal, multi-modal, multi-domain study in stroke.Frontiers in neurology · 2022Article
- Neural computations in children's third-party interventions are modulated by their parents' moral values.NPJ science of learning · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or "components" derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.