Projects

Exploring neurotechnology through embedded systems, neural signal processing, and machine learning.

My projects explore different aspects of neurotechnology—from designing embedded hardware systems and acquiring biological signals to developing machine learning models that interpret neural activity. Together, these projects reflect my long-term goal of building intelligent systems that help us understand and interact with the nervous system.

Selected Projects

Wearable EEG Sensing Platform

Wearable EEG Sensing Platform

Designed and built a custom wearable platform for EEG and physiological signal acquisition. The system integrates custom electronics, embedded firmware, and wireless communication to explore the foundations of wearable neurotechnology.

Built a functional hardware prototype integrating the ADS1299 EEG analog front-end, MAX30102 heart-rate sensor, and LSM6DSO inertial sensor. Designed custom PCBs in KiCad, developed embedded firmware for sensor acquisition, and implemented BLE-based wireless data streaming.

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Neural Signal Decoding using Meta-Learning

Neural Signal Decoding using Meta-Learning

Research Collaboration · Completed

Investigated approaches to improve EEG classification across individuals, addressing one of the major challenges in brain-computer interfaces: inter-subject variability.

Implemented and evaluated meta-learning approaches to study how machine learning models can adapt to variations in EEG patterns between individuals.

Publication →
Neural Signal Decoding from Public EEG Datasets

Neural Signal Decoding from Public EEG Datasets

Independent Research · In Progress

Exploring deep learning approaches for decoding neural activity from publicly available EEG datasets. This project explores machine learning approaches for decoding patterns in EEG signals and evaluating their potential for neural interface applications.

Coming Soon →

Open to new collaborations.

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