Author: Mohit Khamele

Mohit Khamele

Short Bio

Mohit Khamele is a researcher, Ph.D. scholar in Electrical and Electronics Engineering at the Indian Institute of Technology (IIT) Indore, and Project Research Scientist-II working at the intersection of artificial intelligence, biomedical signal processing, and high-frequency communication systems. Mohit holds a Bachelor's in Electronics and Communication Engineering and a Master's in Digital Communication from Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV). His current doctoral and ICMR-funded research at IIT Indore focuses on non-invasive healthcare technologies, including AI-driven mental health alert systems, EEG-based depression detection, and ear-EEG/PPG physiological sensor architectures. Beyond biomedical engineering, his technical expertise extends to RF and microwave engineering, antenna design, and digital education methodologies. As an author for ENTECH Magazine, Mohit shares insights on biomedical AI, signal processing algorithms, RF engineering, and the role of smart sensors in modern healthcare systems.

Extended Bio

Mohit Khamele works at the frontier where electrical engineering meets medicine, building technologies that read the human body without invasive procedures. His research focuses on non-invasive healthcare — using sensors placed on the skin, not inside the body, to gather the signals that reveal our physiological and psychological state.He is a Ph.D. scholar in Electrical and Electronics Engineering at IIT Indore, one of India's premier engineering institutions, and serves as a Project Research Scientist-II on ICMR-funded research projects. His doctoral work targets some of the most pressing challenges in mental healthcare: AI-driven mental health alert systems that can detect early signs of distress, EEG-based depression detection that could make diagnosis more objective and accessible, and ear-EEG/PPG physiological sensor architectures that package multiple sensing modalities into a single, wearable form factor.The ear-EEG/PPG work is particularly innovative. The ear is a surprisingly rich location for physiological sensing, with access to brain activity (via EEG) and blood flow (via PPG) in a position that is stable, unobtrusive, and socially acceptable for long-term wear. Mohit is designing sensor architectures that can continuously monitor both brain and cardiovascular signals from this single location, opening new possibilities for real-time mental health monitoring.His technical foundation is broad. He holds a Master's in Digital Communication and a Bachelor's in Electronics and Communication Engineering from RGPV, giving him expertise in signal processing — the mathematical techniques that extract meaningful information from noisy sensor data. This expertise is critical for biomedical applications, where the signals of interest are often weak and buried in interference.Beyond biomedical engineering, Mohit's expertise extends to RF and microwave engineering, including antenna design for high-frequency communication systems. This background gives him a unique advantage in biomedical sensor design: he understands not just the signal processing algorithms, but the physics of how signals are transmitted, received, and coupled to the human body.He is also interested in digital education methodologies, recognizing that the technologies he develops will only have impact if they are understood by the clinicians, engineers, and students who will use and improve them.As an author for ENTECH Magazine, Mohit writes for the biomedical engineering student exploring career paths, the researcher working on non-invasive sensing technologies, the mental health professional interested in AI-driven diagnostic tools, the RF engineer curious about biomedical applications of their field, and anyone fascinated by the intersection of electronics and human physiology.

Primary Beats

• Biomedical AI & Mental Health Technology: AI-driven systems for mental health monitoring and early intervention; EEG-based detection of depression, anxiety, and cognitive states; machine learning algorithms for interpreting physiological signals; the promise and challenges of AI in mental healthcare.
• Non-Invasive Physiological Sensing (Ear-EEG/PPG Architectures): Designing wearable sensor systems that monitor brain activity and cardiovascular signals from the ear; multi-modal physiological sensing in compact form factors; signal processing for wearable biomedical devices; the future of continuous, unobtrusive health monitoring.
• Signal Processing Algorithms for Biomedical Applications: Mathematical techniques for extracting meaningful information from EEG, ECG, and PPG signals; noise reduction, feature extraction, and classification for physiological data; real-time signal processing for wearable and point-of-care devices.
• RF & Microwave Engineering (Antenna Design, High-Frequency Communication): Antenna design for biomedical and communication applications; RF engineering for sensor systems; the intersection of high-frequency communication and biomedical device design; digital education methodologies for engineering students.