ReviewFrontiers in neuroscience2015
Using neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfalls.
Review in Frontiers in neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Functional neuroimaging of visual creativity: a systematic review and meta-analysis.Brain and behavior · 2016Pooled it
- Explicit and Implicit Responses to Tasting Drinks Associated with Different Tasting Experiences.Sensors (Basel, Switzerland) · 2019Trial
- The Neuro-Cardiac Symbiotic Engine: A Multimodal Fusion Architecture for Cognitive State Decoding via High-Performance Computing.Life (Basel, Switzerland) · 2026Article
- Cognitive state monitoring for neuroadaptive information visualization.Frontiers in human neuroscience · 2026Review
- Automated classification of conversation valence and arousal using autonomic nervous system responses.Frontiers in neuroscience · 2026Article
- Visual cues in MI-BCI induce biases in EEG classification models with deep learning but not with standard machine learning.Frontiers in neuroergonomics · 2026Article
- The Effect of Evaluating Self's Emotions on Frontal Alpha Asymmetry.Brain and behavior · 2025Article
- The impact of cross-validation choices on pBCI classification metrics: lessons for transparent reporting.Frontiers in neuroergonomics · 2025Article
- Neuroergonomic Attention Assessment in Safety-Critical Tasks: EEG Indices and Subjective Metrics Validation in a Novel Task-Embedded Reaction Time Paradigm.Brain sciences · 2024Article
- Investigation of different ML approaches in classification of emotions induced by acute stress.Heliyon · 2024Article
- Reproducible machine learning research in mental workload classification using EEG.Frontiers in neuroergonomics · 2024Review
- Ecological decoding of visual aesthetic preference with oscillatory electroencephalogram features-A mini-review.Frontiers in neuroergonomics · 2024Review
- Robustness of Physiological Synchrony in Wearable Electrodermal Activity and Heart Rate as a Measure of Attentional Engagement to Movie Clips.Sensors (Basel, Switzerland) · 2023Article
- Neuroergonomics on the Go: An Evaluation of the Potential of Mobile EEG for Workplace Assessment and Design.Human factors · 2023Article
- Cognitive effects of prolonged continuous human-machine interaction: The case for mental state-based adaptive interfaces.Frontiers in neuroergonomics · 2022Review
- Using mobile brain/body imaging to advance research in arts, health, and related therapeutics.The European journal of neuroscience · 2021Review
- Unsupervised Clustering of Individuals Sharing Selective Attentional Focus Using Physiological Synchrony.Frontiers in neuroergonomics · 2021Article
- Challenges and Opportunities in Consumer Neuroergonomics.Frontiers in neuroergonomics · 2021Article
- Attention Detection in Virtual Environments Using EEG Signals: A Scoping Review.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Estimating cognitive or affective state from neurophysiological signals and designing applications that make use of this information requires expertise in many disciplines such as neurophysiology, machine learning, experimental psychology, and human factors. This makes it difficult to perform research that is strong in all its aspects as well as to judge a study or application on its merits. On the occasion of the special topic "Using neurophysiological signals that reflect cognitive or affective state" we here summarize often occurring pitfalls and recommendations on how to avoid them, both for authors (researchers) and readers. They relate to defining the state of interest, the neurophysiological processes that are expected to be involved in the state of interest, confounding factors, inadvertently "cheating" with classification analyses, insight on what underlies successful state estimation, and finally, the added value of neurophysiological measures in the context of an application. We hope that this paper will support the community in producing high quality studies and well-validated, useful applications.
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Registered trials
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.