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Multiple classification models including KNN, Logistic Regression, SVM, and Hybrid Classifier models on the TDBrain dataset for the detection and classification of ADHD and MDD

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ADHD_MDD_Classification

This is a collection of classification models (KNN, Logistic Regression, SVM, and Hybrid Classifier models) which are used for the detection and classification of ADHD and MDD with the help of extracting linear and morphological features using EEG signals. There is also a prediction model made using a Voting Classifier.

Setup

-> Upload the ipynb file of choice into either Google Colab (Recommended) or a Jupytr Notebook and run it directly.
-> For training the model, the dataset should be stored in the location you specify in the program.

Dataset

-> Go to the Brainclinics website(https://brainclinics.com/resources/)
-> Download the TDBrain Dataset

Important Notes and Acronyms

We have also provided the Python script that was used to filter out the data for each experiment and can be modified to fit your needs.

EC: Eyes Closed
EO: Eyes Open
CH7: 7 frontal channels
CH26: All primary channels

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Multiple classification models including KNN, Logistic Regression, SVM, and Hybrid Classifier models on the TDBrain dataset for the detection and classification of ADHD and MDD

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