Cargando…
Multi-scale convolutional recurrent neural network for psychiatric disorder identification in resting-state EEG
BACKGROUND: Accurate classification based on affordable objective neuroimaging biomarkers are important steps toward designing individualized treatment. METHODS: In this work, we investigated a deep learning classification model, multi-scale convolutional recurrent neural network (MCRNN), to explore...
Autores principales: | Yan, Weizheng, Yu, Linzhen, Liu, Dandan, Sui, Jing, Calhoun, Vince D., Lin, Zheng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10333510/ https://www.ncbi.nlm.nih.gov/pubmed/37441141 http://dx.doi.org/10.3389/fpsyt.2023.1202049 |
Ejemplares similares
-
Altered Effective Brain Connectivity During Habituation in First Episode Schizophrenia With Auditory Verbal Hallucinations: A Dichotic Listening EEG Study
por: Zheng, Leilei, et al.
Publicado: (2022) -
EEG Resting-State Large-Scale Brain Network Dynamics Are Related to Depressive Symptoms
por: Damborská, Alena, et al.
Publicado: (2019) -
Longitudinal Changes in Neural Connectivity in Patients With Internet Gaming Disorder: A Resting-State EEG Coherence Study
por: Park, Sunyoung, et al.
Publicado: (2018) -
EEG-Based Emotion Recognition by Convolutional Neural Network with Multi-Scale Kernels
por: Phan, Tran-Dac-Thinh, et al.
Publicado: (2021) -
Identification of Major Psychiatric Disorders From Resting-State Electroencephalography Using a Machine Learning Approach
por: Park, Su Mi, et al.
Publicado: (2021)