0000-0001-8305-1417 About
Dr. Daisy Das received her Ph.D. from the Department of Information Technology, Gauhati University, Guwahati, India, in 2026. She was a Visvesvaraya Ph.D. Fellow under the Visvesvaraya Ph.D. Scheme sponsored by the Ministry of Electronics and Information Technology (MeitY), Government of India, throughout her doctoral program.
She is an Assistant Professor at the Royal School of Computer Science and Engineering, Royal Global University.
She received her M.Tech. degree in Information Technology from Gauhati University, Guwahati, India, in 2022, graduating as a Gold Medalist, and her Master of Computer Applications (MCA) degree from Assam Engineering College, Guwahati, India, in 2012.
She is also an active peer reviewer of five international journals in biomedical signal processing and control, neural computing and applications, international journal of psychophysiology, multimedia tools and applications, and biological psychology.
Her research interests include biomedical signal processing, artificial intelligence, machine learning, deep learning, brain–computer interfaces (BCIs), cognitive computing, and AI-driven healthcare technologies.
Employment
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The Assam Royal Global University Assistant Professor2026 - Present
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Gauhati University SRF2024 - Present
Education
Education history is unavailable.
Projects & Funding
Projects & funding information is unavailable.
Publications (20)
- Decreased Beta and Gamma Band Power following Mantra Exposure: An EEG Analysis in Expectant Mothers Save
- Multi-class Epileptic EEG Classification Using a Compact Convolutional Neural Network for Clinical Brain–Computer Interfaces Save
- A deep learning approach for analyzing brainwave signals during audio meditation in expectant mothers: variational mode decomposition with a CNN-BiLSTM model Save
- Exploring EEG biomarkers in meditation, yoga, and mantra practices: a review of neural correlates and methodologies Save
- NeuroFeat: An adaptive neurological EEG feature engineering approach for improved classification of major depressive disorder Save
- EEG-Based ADHD Classification Using Machine Learning and Boosting Algorithms Save
- Deep Hybrid CNN–BiLSTM–Attention Model for EEG Classification Using Wavelet Features Save
- A study on examining prenatal depression with electroencephalography Save
- Inter-Trial Coherence Reveals Enhanced Synchrony During Mantra Listening Save
- Attention-Deficit/Hyperactivity Disorder and Electroencephalography: A Concise Review Save