chenghnn

chenghnn's Stars

• vlawhern/arl-eegmodels

  This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow

  Language:Python1.1k4142282

• gibranfp/P300-CNNT

  1D Convolutional Neural Networks for P300 detection from EEG signals

  Language:Python783119

• MAMEM/eeg-processing-toolbox

  Matlab code for proccesing EEG signals.

  Language:Matlab74151736

• High-East/Attention-based-spatio-temporal-spectral-feature-learning-for-subject-specific-EEG-classification

  Official code for "Attention-Based Spatio-Temporal-Spectral Feature Learning for Subject-Specific EEG Classification" paper

  Language:Python36120

• hyzhang98/DFKM

  Implementation of "Deep Fuzzy K-Means with Adaptive Loss and Entropy Regularization", IEEE Transactions on Fuzzy Systems.

  Language:Python31117

• brainhack-school2020/ADHDsubtypes_project

  ADHD subtypes classification project with 3 different datatypes

  Language:Jupyter Notebook23344

• nmningmei/preprocessing_pipelines

  Preprocessing Pipelines for EEG (MNE-python), fMRI (nipype), MEG (MNE-python/autoreject) data

  Language:Jupyter Notebook22204

• czh513/EEG-Inherent-Fuzzy-Entropy

  EEG Signal Processing - Entropy

  Language:MATLAB16306

• ChristopherSims/FMRI_ADHD_Classification

  3D_CNN classification of ADHD from FMRI data

  Language:Shell150

• HamedAzami/FuzzyEntropy_Matlab

  Here are the Matlab codes used in "Fuzzy Entropy Metrics for the Analysis of Biomedical Signals: Assessment and Comparison, IEEE ACCESS, 2019", including fuzzy entropy with triangular, trapezoidal, Z-shaped, bell-shaped, Gaussian, constant-Gaussian, and exponential functions.

  Language:MATLAB146

• dengemann/meeg-preprocessing

  Preprocessing tools for MEG/EEG

  Language:Python138117

• mwrazam/fatigue-detection-eeg

  Classification of Fatigue in Consumer-grade EEG Using Entropies as Features

  Language:MATLAB130

• jupiek/EEG-ADHD-Project

  In this research, we utilized data from a study that collected EEG data from 41 five-month-old infants with ADHD and 41 infants without ADHD with similar demographic information. The goal of the study is to create an optimal predictive model to identify which parts of the brain contribute heavily to the development of ADHD. Techniques: Data Processing, Logistic Regression, Elastic Net Regression, Random Forest, Cross Validation, Boostrapping, Parameter Tuning

  Language:HTML61

• UBCMint/CaseStudy

  All data and analysis relative to MINT's case study on EEG diagnostics for ADD/ADHD in adults

  Language:Python60

• zhemuzzz/EEG-classification-code

  learn github

  Language:Python6

• ashwinimishra24/Diagnosis-and-prediction-of-ADHD-disease-using-Machine-Learning-method-on-EEG-data

  This repository contains the code and resources for the major project named "Diagnosis and Prediction of ADHD Disease using Machine Learning Methods on EEG Data."

  Language:Jupyter Notebook5

• ArkajyotiPoddar/detection-of-adhd

  We tried to detect ADHD with EEG brain recording data of ADHD and non-ADHD individuals using Random Forest classifier with an accuracy of 97% and also getting some insights to the prediction by the use of Explainable AI algorithms like LIME and SHAP.

  Language:Jupyter Notebook4

• saraMtd/ADHD_Dataset

  **Sample** : * Women (n = 57) * Men (n = 39) * Adhd subtype : hyperactive (n = 2), inattentive (n = 48), mixed (n = 46) ### Types of measures **Conners questionnaire** : standardized questionnaire. Comprizes 66 items about ADHD symptoms and behaviors. Answers are given using a Likert scale (0 = not at all/never and 3 = very often/very frequent). The items are compiled into 4 scales; * inattention/memory (IM) * hyperactivity/restlessness (HR) * impulsivity/emotional lability (IE) * problems with self concept (SC) (refers to self esteem). These four scores are used as the 4 self report symptoms measures. Test-retest correlation for 18-29 years old ranges from 0,8 to 0,92 depending on items. **IVA-II** : Behavioral test. Participants are presented with visual and auditive stimuli (numbers). If the stimulus is 1, whether it is visual or auditive, subjects must click as quickly as possible. If the stimulus is 2, whether it is visual or auditive, subjects must refrain from clickling. Stimuli are presented in a randomized order and at random time. 2 main scales are extracted, comprising 2 subscales each. 1st main scale is Attention Quotient (AQ) and its subscales are AQ auditive and AQ visual. 2nd main scale is Response Control Quotient (RCQ) and its subscales are RCQ auditive and RCQ visual. **Electroencephalography (EEG)** : 19 electrodes caps were used, positioned according to the 10-20 international system and referenced to both ear lobes. Recordings lasted 5 minutes, were participants were instructed to be as still as possible and to keep their eyes opened. The Mitsar System 201 and WinEEG (Mitast) softwares were used for recording. Test-retest and split-half correlations were higher than 0,9.

  Language:Jupyter Notebook4

• RojanAsl/ADHD-Detection-from-EEG-Signal

  Signal Processing project: Extraction of Feature from EEG Signal to Detect ADHD

  Language:Jupyter Notebook21

• Subhikshaa3110/Gohyperactivity

  Exploratory data analysis and different machine learning algorithms were applied to the ADHD dataset from the IEEE data port.

  Language:Jupyter Notebook210

• tahseenat/Depression_detection_usin_CNNandLSTM

  Language:Python21

• hipercog/TOVA-ADHD

  Processing and analysis of TOVA EEG / behavioural data from CENT project

  Language:MATLAB1100

• supratimray/TLSAEEGProjectPrograms

  Language:MATLAB12011