A Novel Framework for Cognitive Load Estimation from Electroencephalogram Signals Utilizing Sparse Representation of Brain Connectivity. - Subrata Pain; Aritra Shome; Tutan Nama; Debasis Samanta; Monalisa Sarma (CVIP (1), 2023)

Adversarial Policy Gradient for Learning Graph-Based Representation in Human Visual Processing. - Subhrasankar Chatterjee; Subrata Pain; Debasis Samanta (Tiny Papers @ ICLR, 2023)

An Efficient Motor Imagery Classification Framework using Sparse Brain Connectivity and Class-consistent Dictionary Learning from Electroencephalogram Signals. - Subrata Pain; Debasis Samanta; Monalisa Sarma (ICVGIP, 2023)

A Novel Graph Representation Learning Approach for Visual Modeling Using Neural Combinatorial Optimization. - Subhrasankar Chatterjee; Subrata Pain; Debasis Samanta (PReMI, 2023)

A Novel Brain Connectivity-Powered Graph Signal Processing Approach for Automated Detection of Schizophrenia from Electroencephalogram Signals. - Subrata Pain; Naincy Vimal; Debasis Samanta; Monalisa Sarma (PReMI, 2023)

Detection of alcoholism by combining EEG local activations with brain connectivity features and Graph Neural Network. - Subrata Pain; Saurav Roy; Monalisa Sarma; Debasis Samanta (Biomed. Signal Process. Control., 2023)

Parametric Sparse Coding of Brain Connectivity Graph Signals for Schizophrenic EEG data. - Subrata Pain; Monalisa Sarma; Debasis Samanta (ICCCNT, 2024)

Graph signal processing and graph learning approaches to Schizophrenia pattern identification in brain Electroencephalogram. - Subrata Pain; Monalisa Sarma; Debasis Samanta (Biomed. Signal Process. Control., 2025)

MSSTNet: A multi-stream time-distributed spatio-temporal deep learning model to detect mind wandering from electroencephalogram signals. - Subrata Pain; Subhrasankar Chatterjee; Monalisa Sarma; Debasis Samanta (Comput. Electr. Eng., 2025)