Work2015

Detection of stress/anxiety state from EEG features during video watching

Giorgos A. Giannakakis · Dimitris Grigoriadis · Manolis Tsiknakis

81 citations0/0 files storedAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Emotion and Mood Recognition EEG and Brain-Computer Interfaces Heart Rate Variability and Autonomic Control

Abstract

This paper studies the effect of stress/anxiety states on EEG signals during video sessions. The levels of arousal and valence that are induced to each subject while watching each video are self rated. These levels are mapped in stress and relaxed states and subjects that fufill criteria of adequate anxiety/stress scale were chosen leading to a subset of 18 subjects. Then, temporal, spectral and non linear EEG features are evaluated for being able to represent accurately states under investigation. Feature selection schemes choose the most significant of them in order to provide increased discrimination ability between relaxed and anxiety/stress states.

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References

1980A circumplex model of affect.2011DEAP: A Database for Emotion Analysis ;Using Physiological Signals 1990What's basic about basic emotions?1986Principles and Practice of Stress Management 2011Four Models of Basic Emotions: A Review of Ekman and Cordaro, Izard, Levenson, and Panksepp and Watt 2010Emotional Stress Recognition System Using EEG and Psychophysiological Signals: Using New Labelling Process of EEG Signals in Emotional Stress State

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2022MuLHiTA: A Novel Multiclass Classification Framework With Multibranch LSTM and Hierarchical Temporal Attention for Early Detection of Mental Stress 2021A study on multi-class anxiety detection using wearable EEG headband 2022Stress Classification Using Brain Signals Based on LSTM Network 2022Human state anxiety classification framework using EEG signals in response to exposure therapy 2020Psychological stimulation for anxious states detection based on EEG-related features 2020Detection of Drivers’ Anxiety Invoked by Driving Situations Using Multimodal Biosignals 2024Deep Learning Approaches for Stress Detection: A Survey

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