S
Shuting Sun
Researcher at Lanzhou University
Publications - 27
Citations - 649
Shuting Sun is an academic researcher from Lanzhou University. The author has contributed to research in topics: Electroencephalography & Computer science. The author has an hindex of 8, co-authored 18 publications receiving 290 citations. Previous affiliations of Shuting Sun include Shandong Normal University.
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EEG-based mild depressive detection using feature selection methods and classifiers
TL;DR: In the spatial distribution of features, left parietotemporal lobe in beta EEG frequency band has greater effect on mild depression detection, and Classification results obtained by GSW + KNN are encouraging and better than previously published results.
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Attention Recognition in EEG-Based Affective Learning Research Using CFS+KNN Algorithm
TL;DR: A classification procedure that combines correlation-based feature selection (CFS) and a k-nearest-neighbor (KNN) data mining algorithm is proposed that had a much better performance than the current CFS+C4.5 algorithm and other classification algorithms.
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EEG-based mild depression recognition using convolutional neural network.
TL;DR: The proposed computer-aided detection (CAD) system using convolutional neural network (ConvNet) provided the accuracy of 85.62% for recognition of mild depression and normal controls with 24-fold cross-validation and can be clinically used for the objective, accurate, and rapid diagnosis of mild Depression.
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Graph Theory Analysis of Functional Connectivity in Major Depression Disorder With High-Density Resting State EEG Data
TL;DR: It was found that the combination of imaginary part of coherence and cluster-span threshold outperformed other methods and right hemisphere function deficiency, symmetry breaking and randomized network structure were found in MDD, which confirmed that MDD had aberrant cognitive processing.
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Depression recognition using machine learning methods with different feature generation strategies.
TL;DR: Experimental results prove the efficiency of the proposed methods and show that EEG could be used as a reliable indicator for depression recognition, which makes it possible for EEG-based portable system design and application in auxiliary depression recognition in the future.