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EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD



Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis.



Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5.


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10.1109/TNSRE.2020.3019063

Deep Spatio-Temporal Representation and Ensemble Classification for Attention Deficit/Hyperactivity Disorder


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10.1007/s13246-021-01018-x

Effects of spectral features of EEG signals recorded with different channels and recording statuses on ADHD classification with deep learning.


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10.4018/ijehmc.2021010106

fMRI Feature Extraction Model for ADHD Classification Using Convolutional Neural Network



EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD


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10.1016/j.bpsc.2018.06.003

Classification Accuracy of Neuroimaging Biomarkers in Attention-Deficit/Hyperactivity Disorder: Effects of Sample Size and Circular Analysis.


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10.1109/TIPTEKNO.2019.8895056

The Effect of Data Augmentation on ADHD Diagnostic Model using Deep Learning



ADHD and the power of generalization: exploring the faces of reification



A deep learning approach with event-related spectral EEG data in attentional deficit hyperactivity disorder



EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD


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10.1080/08856257.2019.1580838

The advantages of an ADHD classification from the perspective of teachers


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10.1007/S10846-018-0890-9

Robot-Assisted ADHD Screening in Diagnostic Process



Identifying ADHD Individuals From Resting-State Functional Connectivity Using Subspace Clustering and Binary Hypothesis Testing.


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10.1007/s10484-019-09439-x

Z-Score Neurofeedback and Heart Rate Variability Training for Adults and Children with Symptoms of Attention-Deficit/Hyperactivity Disorder: A Retrospective Study


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