Psychological stimulation for anxious states detection based on EEG-related features

Anxiety affects human capabilities and behavior as much as it affects productivity and quality of life. It is considered to be the main cause of depression and suicide. Anxious states are detectable by specialists by virtue of their acquired cognition and skills. There is a need for non-invasive rel...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2021-08, Vol.12 (8), p.8519-8533
Main Authors: Baghdadi, Asma, Aribi, Yassine, Fourati, Rahma, Halouani, Najla, Siarry, Patrick, Alimi, Adel
Format: Article
Language:English
Subjects:
Accuracy
Affect (Psychology)
Anxiety
Anxiety disorders
Artificial Intelligence
Classification
Cognition
Computational Intelligence
Computer Science
Electroencephalography
Emotions
Engineering
Experiments
Headsets
Immune system
Original Research
Public speaking
Robotics and Automation
Self assessment
Stress
Students
Support vector machines
Therapy
User Interfaces and Human Computer Interaction
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Summary:Anxiety affects human capabilities and behavior as much as it affects productivity and quality of life. It is considered to be the main cause of depression and suicide. Anxious states are detectable by specialists by virtue of their acquired cognition and skills. There is a need for non-invasive reliable techniques that performs the complex task of anxiety detection. In our study, we investigate the impact of different parameters, notably: trial duration, feature type, feature combination and anxiety levels number. The system is evaluated using our own database containing recorded Electroencephalogram (EEG) signals from 23 participants during anxiety elicitation by means of face-to-face psychological stimuli. EEG signals were captured with an Emotiv Epoc headset as a cost-efficient wireless wearable equipment. Two labeling methods are used and results are presented accordingly. Our findings showed that anxiety is well elicited in 1 s. For Self Assessment Manikan SAM-based detection, Stacked Sparse Autoencoder (SSAE) with different type of features achieves 83.50% and 74.60% for 2 and 4 anxiety levels detection, respectively. Results are improved using the Hamilton-based method. We obtained a rate of 86.7% for 4 levels detection using SSAE. The presented results prove the benefits of the use of a low-cost EEG headset instead of medical non-wireless devices and create a starting point for new researches in the field of anxiety detection.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-020-02586-8