Acoustic-articulatory emotion recognition using multiple features and parameter-optimized cascaded deep learning network
J Li, X Zhang, F Li, S Duan, L Huang - Knowledge-Based Systems, 2024 - Elsevier
Multimodal emotion recognition is an important research direction within artificial
intelligence. In this study, we propose a model for acoustic-articulatory emotion recognition …
intelligence. In this study, we propose a model for acoustic-articulatory emotion recognition …
Evaluation of power spectral and machine learning techniques for the development of subject-specific BCI
Abstract Evaluation and interpretation of massive amounts of brain data are a big challenge
for the design of functional brain-computer interface (BCI) devices. In this chapter, three …
for the design of functional brain-computer interface (BCI) devices. In this chapter, three …
A systematic review on automated clinical depression diagnosis
Assessing mental health disorders and determining treatment can be difficult for a number of
reasons, including access to healthcare providers. Assessments and treatments may not be …
reasons, including access to healthcare providers. Assessments and treatments may not be …
[HTML][HTML] 大学生身体活动水平与抑郁症状的关系
王芃， 王靖， 赵津磊， 王相， 辛鑫， 裘莎丽… - 上海体育大学 …, 2023 - shtyxyxb.xml-journal.net
目的基于静息脑电(EEG) 分析大学生身体活动水平与抑郁症状的关系, 探寻身体活动对抑郁症状
的潜在作用路径. 方法随机招募239 名大学生填写《 抑郁自评量表》《 国际身体活动问卷短卷》 …
的潜在作用路径. 方法随机招募239 名大学生填写《 抑郁自评量表》《 国际身体活动问卷短卷》 …
[HTML][HTML] Early detection of neurological abnormalities using a combined phase space reconstruction and deep learning approach
A Al Fahoum - Intelligence-Based Medicine, 2023 - Elsevier
The scientific literature on depression detection using electroencephalogram (EEG) signals
is extensive and offers numerous innovative approaches. However, these existing state-of …
is extensive and offers numerous innovative approaches. However, these existing state-of …
[HTML][HTML] Improving EEG major depression disorder classification using FBSE coupled with domain adaptation method based machine learning algorithms
H Mohammed, M Diykh - Biomedical Signal Processing and Control, 2023 - Elsevier
Major depression disorder (MDD) has become the leading mental disorder worldwide.
Medical reports have shown that people with depression exhibit abnormal wave patterns in …
Medical reports have shown that people with depression exhibit abnormal wave patterns in …
Current development of biosensing technologies towards diagnosis of mental diseases
The biosensor is an instrument that converts the concentration of biomarkers into electrical
signals for detection. Biosensing technology is non-invasive, lightweight, automated, and …
signals for detection. Biosensing technology is non-invasive, lightweight, automated, and …
A novel variational nonlinear chirp mode decomposition-based critical brain-region investigation for automatic emotion recognition
KS Kamble, J Sengupta - Applied Acoustics, 2023 - Elsevier
The field of affective computing that deals with emotion recognition from physiological
information, particularly electroencephalography (EEG), is becoming more and more …
information, particularly electroencephalography (EEG), is becoming more and more …
Exposure to depression memes on social media increases depressive mood and it is moderated by self-regulation: evidence from self-report and resting EEG …
This study aimed to investigate the effects of depression memes, spread mainly on social
media, on depressive mood, and the moderating role of self-regulation based on self-report …
media, on depressive mood, and the moderating role of self-regulation based on self-report …
Construction of a resting EEG-based depression recognition model for college students and possible mechanisms of action of different types of exercise
L Li, P Wang, S Li, Q Zhao, Z Yin, W Guan, S Chen… - BMC psychiatry, 2023 - Springer
Objectives To investigate the method of resting EEG assessment of depressive symptoms in
college students and to clarify the relationship between physical activity level and …
college students and to clarify the relationship between physical activity level and …