Nandan Tiwari — EEG Researcher & AI Trainer
Advancing AI for
Neural Engineering.
I specialize in visually evoked EEG classification, generative vision, and building interpretable deep learning models. Currently seeking Postdoctoral research opportunities globally.
Bridging the Gap Between AI Theory and Neuroscience.
My doctoral research at the Birla Institute of Technology, sponsored by the prestigious ANRF (SUPRA) Government of India, focuses on extracting meaningful insights from complex brain signals.
By integrating advanced Natural Language Processing (NLP) techniques and Generative Adversarial Networks (GANs) with raw EEG data, I aim to create robust models for brain fingerprinting and image regeneration. I am deeply passionate about open science, reproducible research, and translating complex models into clinical or practical applications.
Technical Expertise
Tools, frameworks, and methodologies I utilize to conduct state-of-the-art research in artificial intelligence and signal processing.
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Students & Professionals Mentored
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Workshops & Seminars Delivered
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High-Impact Publications
AI & EEG Research at BIT Mesra
Core Research Pillars
EEG Signal Analysis
Using Deep learning to train intelligent agents in complex, dynamic environments for advanced EEG cleaning and artifact removal.
Generative Vision
Exploring Generative Adversarial Networks (GANs) for high-fidelity image synthesis and perceived image regeneration from visual stimuli.
Efficient NLP
Developing scalable neural architectures for large language models to improve performance, accessibility, and time-series data analysis.
Publications
Peer-reviewed contributions to the fields of neural engineering and machine learning.
BiGRU-TFA: An Attention-Enhanced Model for EEG Signal Reconstruction using Temporal and Frequency Features
N Tiwari, S Anwar • Electroencephalogram (EEG) signals are often contaminated by artifacts from physiological and nonphysiological sources. Traditional methods for artifact removal struggle with nonstationary noise...
EEG dataset for natural image recognition through visual stimuli
N Tiwari, S Anwar, V Bhattacharjee • Electroencephalography (EEG) is a technique for measuring the brain's electrical activity. Because of its non-invasive nature, it is popular for investigations. This paper presents a novel dataset...
A Review on the Efficacy of Different Data Augmentation Techniques for Deep Learning
N Tiwari, S Anwar • International Conference on Computational Intelligence in Communications...
Neural Decoding of Visual Imagery from EEG Signals: A Comparative Study of Deep Generative Models
N Tiwari, S Anwar • 2025 IEEE Silchar Subsection Conference (SILCON)...
Brain Fingerprinting: From Brain Signal to Forensic Frontier
N Tiwari • Available at SSRN 5483246, 2025...
Brain Fingerprinting and Machine Learning: A Futuristic Approach to Forensic Science
N Tiwari, S Mustafi, S Anwar • 2024 International Conference on Emerging Technologies and Innovation...
An Empirical Analysis on the Effect of Different Parameters in Training a Generative Adversarial Network
S Anwar, N Tiwari • International Conference on MAchine inTelligence for Research & Innovations...
BiGRU-TFA: An Attention-Enhanced Model for EEG Signal Reconstruction using Temporal and Frequency Features
N Tiwari, S Anwar • Electroencephalogram (EEG) signals are often contaminated by artifacts from physiological and nonphysiological sources. Traditional methods for artifact removal struggle with nonstationary noise...
EEG dataset for natural image recognition through visual stimuli
N Tiwari, S Anwar, V Bhattacharjee • Electroencephalography (EEG) is a technique for measuring the brain's electrical activity. Because of its non-invasive nature, it is popular for investigations. This paper presents a novel dataset...
A Review on the Efficacy of Different Data Augmentation Techniques for Deep Learning
N Tiwari, S Anwar • International Conference on Computational Intelligence in Communications...
Neural Decoding of Visual Imagery from EEG Signals: A Comparative Study of Deep Generative Models
N Tiwari, S Anwar • 2025 IEEE Silchar Subsection Conference (SILCON)...
Brain Fingerprinting: From Brain Signal to Forensic Frontier
N Tiwari • Available at SSRN 5483246, 2025...
Brain Fingerprinting and Machine Learning: A Futuristic Approach to Forensic Science
N Tiwari, S Mustafi, S Anwar • 2024 International Conference on Emerging Technologies and Innovation...
An Empirical Analysis on the Effect of Different Parameters in Training a Generative Adversarial Network
S Anwar, N Tiwari • International Conference on MAchine inTelligence for Research & Innovations...
Academic & Professional Journey
Ph.D. Research Scholar
Birla Institute of Technology, Mesra
Sponsored Project: ANRF (SUPRA) Government of India.
Focus: Visually Evoked EEG Classification and Perceived Image Regeneration using Deep Learning for Brain Fingerprinting.
- Designing novel deep learning architectures for high-dimensional timeseries biological data.
- Publishing peer-reviewed articles in high-impact Q1/Q2 IEEE and Elsevier journals.
Member, Board of Advisors
Henry Harvin Education
Guiding syllabus and training improvements by ensuring curriculum relevance, industry alignment, and integration of emerging AI/ML research trends for professional learners.
Machine Learning Trainer
Cetpa Infotech PVT. LTD
Started as a trainee handling data preprocessing and model evaluation, rapidly progressing to Trainer role. Conducted comprehensive workshops to upskill engineering professionals in applied Machine Learning workflows.