… ADHD children from normal children using their EEG signals … of ADHD, which was confirmed
in an independent ADHD … using EEG analysis in the classification and diagnosis of ADHD, it …

… We propose an approach for ADHD classification based on LSTM deep learning. The
highest accuracy was obtained as 92.15% in the eyes-closed resting state. When the LSTM …

… emerging EEG metrics and their reliability in diagnostic classification. Considering the clinical
heterogeneity of ADHD and the complexity of information available from the EEG signals, …

… high classification rate between ADHD and BMD patients using a suitable classifier to their
EEG … In this study the aim is to classify BMD from ADHD patients based on their EEG features …

… EEG signals for the diagnosis of ADHD. For this purpose, we want to compare morphological,
time, frequency, time-frequency, and nonlinear features for EEG classification of ADHD. …

… We used two different models to classify the EEG signals according to their … the EEG signals
for players using EMOTIV should look more like each other, compared to the EEG signals for …

… We used EEG signals recorded from 31 ADHD children and 30 healthy children. In this
study… For this purpose, we first preprocessed EEG signals to eliminate noise and artifacts. Then …

… of rest state EEG signals for ADHD classification, brain activity … making a clear distinction
between ADHD and normal subjects. … of EEG signals to act as objective biomarkers of ADHD. …

… EEG signals of children with and without ADHD in this study have currently portrayed a
regularity (non-randomity) geometry similar to the Mandala patterns, which represents the …

… children and the children with ADHD using a dPTE-based … Matrices (ECM) between EEG
signals obtained by dPTE measure … that classified the healthy children and those with ADHD …