ArticleSensors (Basel, Switzerland)2019
Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- A logic-based knowledge-driven bidirectional multi-attention GRU framework for fear level classification in humans.Scientific reports · 2026Article
- Are you afraid? Neurophysiological responses to multidimensional fear in an interactive semi-immersive environment.Frontiers in systems neuroscience · 2026Article
- Fear Detection Using Electroencephalogram and Artificial Intelligence: A Systematic Review.Brain sciences · 2025Review
- Negative emotion modulates postural tremor variability in Parkinson's disease: A multimodal EEG and motion sensor study toward behavioral interventions.IBRO neuroscience reports · 2025Article
- SpiderPhy dataset: A multimodal dataset of Physiological, Psychometric and Behavioral Responses to fear stimuli.Scientific data · 2025Article
- Emotional recognition technologies applied to health: review and challenges.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2025Review
- The healing power ofFrontiers in psychology · 2025Article
- Emotional salience network involved in constructing two-dimensional fear space in humans.Neurobiology of stress · 2025Review
- Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features.Sensors (Basel, Switzerland) · 2024Article
- Investigation of different ML approaches in classification of emotions induced by acute stress.Heliyon · 2024Article
- Multi-Input CNN-LSTM deep learning model for fear level classification based on EEG and peripheral physiological signals.Frontiers in psychology · 2023Article
- A Data-Driven Adaptive Emotion Recognition Model for College Students Using an Improved Multifeature Deep Neural Network Technology.Computational intelligence and neuroscience · 2022Article
- Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.Brain sciences · 2021Review
- Effects of Coronavirus (COVID-19) Fear on Oral Health Status.Oral health & preventive dentistry · 2021Article
- Machine Learning Methods for Fear Classification Based on Physiological Features.Sensors (Basel, Switzerland) · 2021Article
- Fear Recognition for Women Using a Reduced Set of Physiological Signals.Sensors (Basel, Switzerland) · 2021Article
- Effect of Fragrant Primula Flowers on Physiology and Psychology in Female College Students: An Empirical Study.Frontiers in psychology · 2021Article
- COVID-19 pandemic and pediatric dentistry: Fear, eating habits and parent's oral health perceptions.Children and youth services review · 2020Article
- EEG-Based BCI Emotion Recognition: A Survey.Sensors (Basel, Switzerland) · 2020Review
- Physiological Sensors Based Emotion Recognition While Experiencing Tactile Enhanced Multimedia.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
There has been steady progress in the field of affective computing over the last two decades that has integrated artificial intelligence techniques in the construction of computational models of emotion. Having, as a purpose, the development of a system for treating phobias that would automatically determine fear levels and adapt exposure intensity based on the user's current affective state, we propose a comparative study between various machine and deep learning techniques (four deep neural network models, a stochastic configuration network, Support Vector Machine, Linear Discriminant Analysis, Random Forest and k-Nearest Neighbors), with and without feature selection, for recognizing and classifying fear levels based on the electroencephalogram (EEG) and peripheral data from the DEAP (Database for Emotion Analysis using Physiological signals) database. Fear was considered an emotion eliciting low valence, high arousal and low dominance. By dividing the ratings of valence/arousal/dominance emotion dimensions, we propose two paradigms for fear level estimation-the two-level (0-
Indexed as
Identifiers
What OpenQuestion holds
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.