Abhinav Sattiraju

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data

This study improves major depressive disorder diagnosis using raw multichannel EEG with transfer learning from single-channel sleep stage data, achieving better performance than previous methods.

An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-based Schizophrenia Diagnosis

This study develops a robust and explainable deep learning model for schizophrenia diagnosis using EEG data, showing improved performance and prioritization of specific brain regions and frequency bands.

Novel Approach Explains Spatio-Spectral Interactions in Raw Electroencephalogram Deep Learning Classifiers

This study introduces a novel explainability approach for rs-EEG deep learning classifiers, revealing spatio-spectral interactions in MDD diagnosis.

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder by Pretraining Deep Learning Models with Single Channel Sleep Stage Data

A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data

This paper introduces a framework for evaluating deep learning classifiers on raw EEG data, focusing on spatial, spectral, and temporal explainability, and applies it to MDD diagnosis.

Examining Reproducibility of EEG Schizophrenia Biomarkers Across Explainable Machine Learning Models

This study trains and compares random forest and SVM models to identify reproducible EEG biomarkers for schizophrenia using spectral features from resting state data.

Examining Effects of Schizophrenia on EEG with Explainable Deep Learning Models

This study applies explainable deep learning models to raw EEG data to diagnose schizophrenia, identifying key spectral and spatial features that align with prior research.