Proceedings ArticleDOI
EEG signal and video analysis based depression indication
Yashika Katyal,Suhas V Alur,Shipra Dwivedi,R. Menaka +3 more
- pp 1353-1360
TLDR
A novel method for combining both EEG signal analysis and facial emotion recognition through video analysis to successfully categorize depression into various levels is described.Abstract:
Depression is a common phenomenon in the present scenario. Due to the fast pace at which our lives move and immense pressure that we face adolescents, office goers and even the elders face depression. Diagnosing depression in the early curable stages is very important and may even save the life of a patient. EEG signal analysis has been used for medical research like epilepsy, sleep disorder, insomnia etc. Similarly, video signal analysis has been used for facial features detection, eye movement, emotion recognition etc. Collaborating both the methods accuracy of depression detection can be improved upon. This paper describes a novel method for combining both EEG signal analysis and facial emotion recognition through video analysis to successfully categorize depression into various levels. For this aim, power spectrum of three frequency bands (alpha, beta, and theta) and the whole bands of EEG are used as features along with standard deviation, mean and entropy.read more
Citations
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Journal ArticleDOI
Automatic Assessment of Depression Based on Visual Cues: A Systematic Review
Anastasia Pampouchidou,Panagiotis G. Simos,Kostas Marias,Fabrice Meriaudeau,Fan Yang,Matthew Pediaditis,Manolis Tsiknakis +6 more
TL;DR: The review outlines methods and algorithms for visual feature extraction, dimensionality reduction, decision methods for classification and regression approaches, as well as different fusion strategies, for automatic depression assessment utilizing visual cues alone or in combination with vocal or verbal cues.
Journal ArticleDOI
Attention-based convolutional neural network and long short-term memory for short-term detection of mood disorders based on elicited speech responses
TL;DR: This study proposed an approach for short-term detection of mood disorders based on elicited speech responses and found that CNN- and LSTM-based attention models improved the mood disorder detection accuracy of the proposed method by approximately 11%.
Journal ArticleDOI
EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks:A review.
TL;DR: In this paper, a comprehensive review on the two mental disorders: Major depressive disorder (MDD) and Bipolar disorder (BD) with noteworthy publications during the last ten years is presented, focusing on the literature works adopting neural networks fed by EEG signals.
Journal ArticleDOI
Cell-Coupled Long Short-Term Memory With $L$ -Skip Fusion Mechanism for Mood Disorder Detection Through Elicited Audiovisual Features
TL;DR: An elicitation-based approach is proposed for realizing a one-time diagnosis of bipolar disorder by using responses elicited from patients by having them watch six emotion-eliciting videos.
Proceedings ArticleDOI
Neurofeedback training content for treatment of stress
TL;DR: In this article, the authors presented a comprehensive and critical summary of available contents for neurofeedback training and established a need for the development of content as a stimulus for neuro-feedback which trains the subject on how to control his brain activity, especially in stress condition.
References
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