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Depression recognition using machine learning methods with different feature generation strategies
X Li, X Zhang, J Zhu, W Mao, S Sun, Z Wang… - Artificial intelligence in …, 2019 - Elsevier
The diagnosis of depression almost exclusively depends on doctor-patient communication
and scale analysis, which have the obvious disadvantages such as patient denial, poor …
and scale analysis, which have the obvious disadvantages such as patient denial, poor …
Enhancing EEG-based classification of depression patients using spatial information
C Jiang, Y Li, Y Tang, C Guan - IEEE transactions on neural …, 2021 - ieeexplore.ieee.org
Background: Depression has become a leading mental disorder worldwide. Evidence has
shown that subjects with depression exhibit different spatial responses in …
shown that subjects with depression exhibit different spatial responses in …
EEG-based mild depression recognition using convolutional neural network
X Li, R La, Y Wang, J Niu, S Zeng, S Sun… - Medical & biological …, 2019 - Springer
Electroencephalography (EEG)–based studies focus on depression recognition using data
mining methods, while those on mild depression are yet in infancy, especially in effective …
mining methods, while those on mild depression are yet in infancy, especially in effective …
Stress classification by multimodal physiological signals using variational mode decomposition and machine learning
In this pandemic situation, importance and awareness about mental health are getting more
attention. Stress recognition from multimodal sensor based physiological signals such as …
attention. Stress recognition from multimodal sensor based physiological signals such as …
Multimodal mild depression recognition based on EEG-EM synchronization acquisition network
J Zhu, Y Wang, R La, J Zhan, J Niu, S Zeng… - IEEE Access, 2019 - ieeexplore.ieee.org
In this paper, we used electroencephalography (EEG)-eye movement (EM) synchronization
acquisition network to simultaneously record both EEG and EM physiological signals of the …
acquisition network to simultaneously record both EEG and EM physiological signals of the …
Investigation of the neural correlation with task performance and its effect on cognitive load level classification
Electroencephalogram (EEG)-based cognitive load assessment is now an important
assignment in psychological research. This type of research work is conducted by providing …
assignment in psychological research. This type of research work is conducted by providing …
Ultra-Wideband (UWB) characteristic estimation of elliptic patch antenna based on machine learning techniques
In this study, estimation of Ultra-Wideband (UWB) characteristics of microstrip elliptic patch
antenna is investigated by means of k-nearest neighborhood algorithm. A total of 16,940 …
antenna is investigated by means of k-nearest neighborhood algorithm. A total of 16,940 …
Human-computer-interface for controlling the assistive technology device
Imagining a motion without doing the actual movement is known as Motor imaginary (MI).
However, translating MI as an input for BCI that control the Assistive Technology (AT) device …
However, translating MI as an input for BCI that control the Assistive Technology (AT) device …
A hierarchical fuzzy system for an advanced driving assistance system
In this study, we present a hierarchical fuzzy system by evaluating the risk state for a Driver
Assistance System in order to contribute in reducing the road accident's number. A key …
Assistance System in order to contribute in reducing the road accident's number. A key …
Synergizing Ensemble and Hybrid Learing for Advanced Automated Depression Detection.
Electroencephalogram (EEG) signals have been the subject of much interest lately, with
applications in affective computing, medicine, and other related fields. Depression has been …
applications in affective computing, medicine, and other related fields. Depression has been …