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ArticleHuman stress classification using EEG signals in response to music tracks

Anum Asif2019

Article2019

Human stress classification using EEG signals in response to music tracks

Anum Asif · Muhammad Majid · Syed Muhammad Anwar

217 citations·0/0 files stored·Computers in Biology and Medicine

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Music Therapy and Health

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References

1983A Global Measure of Perceived Stress 2017State-Trait Anxiety Inventory 2006Emotional speech recognition: Resources, features, and methods 2003Speech emotion recognition using hidden Markov models 2008The Anxiety‐ and Pain‐Reducing Effects of Music Interventions: A Systematic Review 2013The Effect of Music on the Human Stress Response 2012Objective measures, sensors and computational techniques for stress recognition and classification: A survey 2001Relaxing Music Prevents Stress-Induced Increases in Subjective Anxiety, Systolic Blood Pressure, and Heart Rate in Healthy Males and Females

Cited by

2019Classification of Perceived Mental Stress Using A Commercially Available EEG Headband 2022Frontal lobe real-time EEG analysis using machine learning techniques for mental stress detection 2020EEG Based Classification of Long-Term Stress Using Psychological Labeling 2020An Effective Mental Stress State Detection and Evaluation System Using Minimum Number of Frontal Brain Electrodes 2022Recognising situation awareness associated with different workloads using EEG and eye-tracking features in air traffic control tasks

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Details

Year2019

Typearticle

Published2019-02-25

VenueComputers in Biology and Medicine

ISSN-L0010-4825

Languageen

Citations217

Files0 public / 0

ShelfEmotion and Mood Recognition

Identifiers

doi10.1016/j.compbiomed.2019.02.015

openalex_workW2916033447

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2020The Impact of Different Sounds on Stress Level in the Context of EEG, Cardiac Measures and Subjective Stress Level: A Pilot Study

2022Human Stress Detection With Wearable Sensors Using Convolutional Neural Networks

2021Human stress classification during public speaking using physiological signals