ArticleBrain sciences2019
A Hybrid Feature Pool-Based Emotional Stress State Detection Algorithm Using EEG Signals.
Article in Brain sciences, 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.
- Neonatal jaundice detection using a vision transformer-based deep learning model.Scientific reports · 2026Article
- Brain-inspired signal processing for detecting stress during mental arithmetic tasks.Brain informatics · 2025Article
- A Novel Optimized Hybrid Deep Learning Framework for Mental Stress Detection Using Electroencephalography.Brain sciences · 2025Article
- 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
- Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals.Diagnostics (Basel, Switzerland) · 2023Article
- ECG and EEG based detection and multilevel classification of stress using machine learning for specified genders: A preliminary study.PloS one · 2023Article
- Cross-Subject Emotion Recognition Using Fused Entropy Features of EEG.Entropy (Basel, Switzerland) · 2022Article
- Article
- Analysis of Emotional Stress of Teachers in Japanese Teaching Process Based on EEG Signal Analysis.Occupational therapy international · 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
- An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes.Diagnostics (Basel, Switzerland) · 2020Article
- Attention-based multi-feature fusion neuromarker for EEG-driven stress classification in learners.International journal of clinical and health psychology : IJCHPArticle
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Authors and funding
2 authors.
Funding
Abstract
Human stress analysis using electroencephalogram (EEG) signals requires a detailed and domain-specific information pool to develop an effective machine learning model. In this study, a multi-domain hybrid feature pool is designed to identify most of the important information from the signal. The hybrid feature pool contains features from two types of analysis: (a) statistical parametric analysis from the time domain, and (b) wavelet-based bandwidth specific feature analysis from the time-frequency domain. Then, a wrapper-based feature selector, Boruta, is applied for ranking all the relevant features from that feature pool instead of considering only the non-redundant features. Finally, the k-nearest neighbor (k-NN) algorithm is used for final classification. The proposed model yields an overall accuracy of 73.38% for the total considered dataset. To validate the performance of the proposed model and highlight the necessity of designing a hybrid feature pool, the model was compared to non-linear dimensionality reduction techniques, as well as those without feature ranking.
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