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Publications

Towards development of prediction models for depression using EEG data

Comparing resting state and task-based EEG using machine learning to predict vulnerability to depression in a non-clinical population

Motor Activity Recognition Using Eeg Data and Ensemble of Stacked BLSTM-LSTM Network and Transformer Model

Understanding and predicting future relapse in depression from resting state functional connectivity and self-referential processing

Decoding the cognitive states of attention and distraction in a real-life setting using EEG

Decoding the Mental States of Focus and Distraction in a Real Life Setting of Tibetan Monastic Deabtes Using EEG and Machine Learning

EEG-based age and gender prediction using deep BLSTM-LSTM network model

Multimodal Gait Recognition With Inertial Sensor Data and Video Using Evolutionary Algorithm

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