ArticleInternational journal of environmental research and public health2018
Emotional Stress State Detection Using Genetic Algorithm-Based Feature Selection on EEG Signals.
Article in International journal of environmental research and public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.Journal of medical systems · 2019Pooled it
- NeuroNetFusion: enhanced EEG abnormality classification via multi-network TF-IDF feature selection.Scientific reports · 2026Article
- An Effective and Interpretable EEG-Based Depression Recognition Method Using Hybrid Feature Selection.Bioengineering (Basel, Switzerland) · 2026Article
- Cognitive load detection through EEG lead wise feature optimization and ensemble classification.Scientific reports · 2025Article
- Lobe-wise cognitive load detection using empirical Fourier decomposition and optimized machine learning.Frontiers in physiology · 2025Article
- The Effect of Stress on a Personal Identification System Based on Electroencephalographic Signals.Sensors (Basel, Switzerland) · 2024Article
- Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters.Brain sciences · 2023Article
- Mental stress recognition on the fly using neuroplasticity spiking neural networks.Scientific reports · 2023Article
- Enhancing biofeedback-driven self-guided virtual reality exposure therapy through arousal detection from multimodal data using machine learning.Brain informatics · 2023Article
- A novel physiological feature selection method for emotional stress assessment based on emotional state transition.Frontiers in neuroscience · 2023Article
- Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA.Entropy (Basel, Switzerland) · 2022Article
- Article
- Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM).Sensors (Basel, Switzerland) · 2022Article
- Genetic Algorithm-Based Human Mental Stress Detection and Alerting in Internet of Things.Computational intelligence and neuroscience · 2022Article
- EEG diagnosis of depression based on multi-channel data fusion and clipping augmentation and convolutional neural network.Frontiers in physiology · 2022Article
- Enhancing EEG-Based Mental Stress State Recognition Using an Improved Hybrid Feature Selection Algorithm.Sensors (Basel, Switzerland) · 2021Article
- EEG Mental Stress Assessment Using Hybrid Multi-Domain Feature Sets of Functional Connectivity Network and Time-Frequency Features.Sensors (Basel, Switzerland) · 2021Article
- A Review on Mental Stress Assessment Methods Using EEG Signals.Sensors (Basel, Switzerland) · 2021Review
- Detection of Negative Stress through Spectral Features of Electroencephalographic Recordings and a Convolutional Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Feature Selection Model based on EEG Signals for Assessing the Cognitive Workload in Drivers.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, stress analysis by using electro-encephalography (EEG) signals incorporating machine learning techniques has emerged as an important area of research. EEG signals are one of the most important means of indirectly measuring the state of the brain. The existing stress algorithms lack efficient feature selection techniques to improve the performance of a subsequent classifier. In this paper, genetic algorithm (GA)-based feature selection and
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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.