Sagnik De

I completed my B.Tech in Electronics and Communication Engineering in 2025, from Institute of Radio Physics and Electronics (IRPE), University of Calcutta, India. I also completed my Minors in Business Administration from Indian Institute of Technology (IIT), Patna.

I was a Winter Research Intern at IIT Delhi with Prof. Tapan Kumar Gandhi , focusing on graph based deep learning framework for anxiety state detection from neural signals. Further, I analyzed intercranial EEG (iEEG) data for real-time epileptic seizure prediction in humans.

I also served as a Research Intern at MANIT Bhopal under Dr. Varun Bajaj , contributing to EEG-based identification of dementia subtypes, and at IIIT Naya Raipur under Dr. Anurag Singh , where I developed multimodal deep learning methods for Major Depressive Disorder diagnosis. Earlier, I worked at CDAC Pune with Dr. Anil Kumar Gupta on EEG-based early detection of Parkinson’s Disease and pathological brain state classification. Further, at University of Calcutta, under Dr. Anisha Halder Roy , I worked on multimodal EEG–sEMG fusion for chronic Lower Back pain assessment, performed dedicated neuropathic pain analysis using EEG signals, and investigated brain activity patterns during olfactory and gustatory perception.

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My primary research interests span Artificial Intelligence & IoT in Healthcare, Biomedical Signal Processing, Brain-Computer Interface (BCI), Neural Engineering, Computational Neuroscience and Medical Image Analysis. I am deeply fascinated by the potential of these fields to shape the future of technology and transform the way we interact with machines and information.

I am always open to new collaborations and research ideas. Feel free to reach out if you are interested in working together!

2025

	A Novel Vision Transformer based Multimodal Fusion Approach for Clinical MDD Diagnosis Using EEG and Audio Signals (NEW!) Sagnik De, Anurag Singh, Ashish Kumar Bhandari IEEE Transactions on Computational Biology and Bioinformatics, 2025 Abstract / Article Major Depressive Disorder (MDD) is a debilitating mental health condition characterized by persistent sadness, anhedonia, and cognitive impairments that significantly disrupt daily functioning. Accurate diagnosis remains difficult due to the subjective nature of clinical assessments, highlighting the need for objective and automated diagnostic tools. Hence, this study proposes a novel multimodal framework integrating electroencephalography (EEG) and audio signals for accurate MDD detection.
	GLEAM: A Multimodal Deep Learning Framework for Chronic Lower Back Pain Detection Using EEG and sEMG Signals Sagnik De, Prithwijit Mukherjee, Anisha Halder Roy Computers in Biology and Medicine, 2025 Abstract / Article Low Back Pain (LBP) is the most prevalent musculoskeletal condition worldwide and a leading cause of disability, significantly affecting mobility, work productivity, and overall quality of life.
	TasteNet: A Novel Deep Learning Approach for EEG-Based Basic Taste Perception Recognition Using CEEMDAN Domain Entropy Features Sagnik De, Prithwijit Mukherjee, Anisha Halder Roy Journal of Neuroscience Methods, 2025 Abstract / Article Taste perception is the process by which the gustatory system detects and interprets chemical stimuli from food and beverages, involving activation of taste receptors on the tongue.
	Identification of patients with de novo Parkinson's Disease from chemosensory EEG signals using ICEEMDAN domain Entropy Features (Spotlight Paper!) Sagnik De, Sreenija Pavuluri, Anil Kumar Gupta IEEE Sensors Letters, 2025 Abstract / Article / Spotlight Parkinson's disease (PD) is a progressive neurodegenerative disorder that impairs motor and sensory functions, with early symptoms often involving olfactory dysfunction.
	Quantifying the Impact of Speaker and Content Features on ASR Systems Using Unsupervised Distance Metrics Sreenija Pavuluri, Sagnik De, Anil Kumar Gupta IEEE Sensors Reviews, 2025 Abstract / Article Automatic speech recognition (ASR) models have become increasingly sophisticated, yet the underlying mechanisms driving their translation accuracy remain underexplored.

2024

SLiTRANet: An EEG-Based Automated Diagnosis Framework for Major Depressive Disorder Monitoring Using a Novel LGCN and Transformer-Based Hybrid Deep Learning Approach Sagnik De, Anurag Singh, Vivek Tiwari, Harshita Patel, G. N. Vivekananda, Dharmendra Singh Rajput IEEE Access, 2024 Abstract / Article

Awards & Achievements

Recipient of IASc-INSA-NASI Summer Research Fellowship Program 2024

Recipient of Satyendra Nath Bose Summer Research Internship Program 2024, NIT Silchar

Awarded the Outstanding Volunteer 2023-24 by IEEE Calcutta University Student Branch

Won the 3rd Runners Up in TELECAST 2024 organized by University of Calcutta, Kolkata in collaboration with CTiF, India

Received the Best Paper Award at IEEE ICCCCM 2024 and IEEE IACIS 2024

Won the 1st Prize in COGNITECH 2023 organized by AI & Robotics Club in collaboration with IEEE Calcutta University Student Branch

Won the 1st Prize in Research Work Presentation 2023 organized by IEEE Photonics Society Kolkata Chapter, IEEE APS Kolkata Chapter & IEEE Calcutta University Student Branch

Reviewer

Positions of Responsibility

© Sagnik De (2025) | A person who never made a mistake never tried anything new