Gourav Siddhad

About

I am a Postdoctoral Fellow at Yoshimura Lab, Institute of Science Tokyo (Science Tokyo), working under the mentorship of Prof. Natsue Yoshimura and Dr. Laura Martinez. My research focuses on the intersection of human behavior and technology, specifically leveraging Brain-Computer Interfaces (BCI) and Deep Learning to solve complex real-world problems.

I recently completed my Postdoctoral Fellowship at HAIx Lab, Indian Institute of Technology Gandhinagar (IITGN), working under the mentorship of Dr. Yogesh Kumar Meena. I earned my Ph.D. from IIT Roorkee under the supervision of Prof. Partha Pratim Roy. My doctoral research, "Towards Human Behavior Prediction using EEG: Analyzing Cognitive Workload and Emotional Responses," focused on decoding human cognitive states through physiological signals. Prior to my Ph.D., I completed my M.Tech at PDPM IIITDM Jabalpur, where I specialized in Cancelable Biometrics for low-end devices under the supervision of Prof. Pritee Khanna (Professor, CSE, PDPM IIITDM Jabalpur), and my B.Tech at Hansraj College, University of Delhi.

I am passionate about building intelligent systems that understand human intent and behavior. I am always open to discussing research collaborations or technical innovations in the AI/ML space.

Core Research Interests: Brain-Computer Interface (BCI) & EEG Analysis | Computer Vision & Deep Learning | Cancelable Biometrics & Data Security

Employment

• Institute of Science Tokyo Post Doctoral Fellow
  2026 - Present
• Indian Institute of Technology Gandhinagar Post Doctoral Fellow
  2026 - 2026

Education

• Indian Institute of Technology Roorkee Doctor of Philosophy
  2020 - 2025
• Indian Institute of Information Technology Design and Manufacturing Jabalpur Master of Technology
  2018 - 2020
• University of Delhi Bachelor of Technology
  2013 - 2017

Projects & Funding

Projects & funding information is unavailable.

Publications (31)

• Enhancing EEG Signal-Based Emotion Recognition With Synthetic Data: Diffusion Model Approach
  IEEE Transactions on Artificial Intelligence 2026 DOI: 10.1109/TAI.2025.3641576
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• Modified TSception for Analyzing Driver Drowsiness and Mental Workload from EEG
  Arxiv 2026 DOI: 10.48550/arXiv.2512.21747
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• SwitchBraidNet: Quantisation-Aware Lightweight Architecture for Hybrid Brain-Computer Interface
  ArXiv 2026
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• SwitchBraidNet: Quantisation-Aware Lightweight Architecture for Hybrid Brain-Computer Interface
  Arxiv 2026 DOI: 10.48550/arXiv.2606.18816
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• Modified TSception for analyzing driver drowsiness and mental workload from EEG
  Neural Computing and Applications 2026 DOI: 10.1007/s00521-026-12086-z
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• MECASA: Motor Execution Classification using Additive Self-Attention for Hybrid EEG-fNIRS Data
  arXiv e-prints 2025 DOI: 10.48550/arXiv.2501.05525
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• Awake at the Wheel: Enhancing Automotive Safety Through EEG-Based Fatigue Detection
  2025 DOI: 10.1007/978-3-031-78195-7_23
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• DrowzEE-G-Mamba: Leveraging EEG and State Space Models for Driver Drowsiness Detection
  2025 DOI: 10.1007/978-3-031-78398-2_19
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• Modified TSception for Analyzing Driver Drowsiness and Mental Workload from EEG
  2025
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• Neural Networks Meet Neural Activity: Utilizing EEG for Mental Workload Estimation
  2025 DOI: 10.1007/978-3-031-78195-7_22
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