ArticleComputational intelligence and neuroscience2020
Modified Support Vector Machine for Detecting Stress Level Using EEG Signals.
Article in Computational intelligence and neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 62 citations in OpenAlex.
- Mental Stress Detection Using Physiological Sensors and Artificial Intelligence: A Review.Sensors (Basel, Switzerland) · 2026Review
- 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
- Applying artificial intelligence on EDA sensor data to predict stress on minimally invasive robotic-assisted surgery.International journal of computer assisted radiology and surgery · 2024Article
- Applications of artificial intelligence-machine learning for detection of stress: a critical overview.Molecular psychiatry · 2024Review
- External globus pallidus input to the dorsal striatum regulates habitual seeking behavior in male mice.Nature communications · 2023Article
- Mental health issues assessment using tools during COVID-19 pandemic.Innovations in systems and software engineering · 2022Article
- LightFD: Real-Time Fault Diagnosis with Edge Intelligence for Power Transformers.Sensors (Basel, Switzerland) · 2022Article
- Stress Classification Using Brain Signals Based on LSTM Network.Computational intelligence and neuroscience · 2022Article
- Prediction of Automobile Wiper Motor Noise Based on Support Vector Machine with Vibration Sensors.Computational intelligence and neuroscience · 2022Article
- Real-Time Stress Level Feedback from Raw Ecg Signals for Personalised, Context-Aware Applications Using Lightweight Convolutional Neural Network Architectures.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stress is categorized as a condition of mental strain or pressure approaches because of upsetting or requesting conditions. There are various sources of stress initiation. Researchers consider human cerebrum as the primary wellspring of stress. To study how each individual encounters stress in different forms, researchers conduct surveys and monitor it. The paper presents the fusion of 5 algorithms to enhance the accuracy for detection of mental stress using EEG signals. The Whale Optimization Algorithm has been modified to select the optimal kernel in the SVM classifier for stress detection. An integrated set of algorithms (NLM, DCT, and MBPSO) has been used for preprocessing, feature extraction, and selection. The proposed algorithm has been tested on EEG signals collected from 14 subjects to identify the stress level. The proposed approach was validated using accuracy, sensitivity, specificity, 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.