Biosignal Machine Learning & Edge AI

Project Overview

Field / Domain: Biomedical Signal Processing & Edge Computing
Role / Scope: Machine Learning Research, Neural Architecture Design & Embedded Edge Deployment
Technologies: Python, PyTorch, TensorFlow Lite, OpenCV, Scikit-Learn, Single Board Computers (SBCs)

The Architectural Challenge

Processing high-dimensional, noisy biosignals (EEG for brain-computer interfaces, EMG for assistive robotics and prosthetic control) demands robust feature extraction and machine learning models that can generalize across different human subjects while operating within strict low-latency constraints on embedded hardware.

Technical Solution

• Signal Processing & Feature Engineering: Developed specialized mathematical feature extraction pipelines for multi-channel biosignal data, evaluating time-domain, frequency-domain, and spatial filter configurations.
• Transfer Learning & Deep Architectures: Formulated deep neural network and autonomous transfer learning algorithms that adapt models to new subjects with minimal calibration data, outperforming standard baseline classifiers.
• Embedded Edge Deployment: Quantized and compiled trained neural network architectures using TensorFlow Lite and OpenCV to run real-time inference directly on Single Board Computers (SBCs) and microcontrollers.
• Empirical Academic Rigor: Co-authored 8 peer-reviewed research papers in machine learning, biomedical engineering, and assistive robotics, validating experimental conclusions against international benchmark datasets.

Demonstrated Outcome

Demonstrated real-time, high-accuracy finger movement and robotic control using biosignals on resource-constrained edge hardware, published in peer-reviewed academic literature.

Inquire About Machine Learning & Edge AI

Exploring biosignal processing, custom model architectures, or edge AI deployment?

Discuss Your Project or reach our engineering team directly at contact@antardata.com.