This repository offers a pipeline for classifying insomnia using EEG, EMG, EOG, and ECG signals, featuring early and late fusion, signal preprocessing, feature extraction, and machine learning models for accurate detection.

Production-grade ensemble framework combining XGBoost, PyTorch & Sklearn - 70%+ test coverage with Optuna optimization for time-series prediction

First‑year BTech Electrical Engineering project (2020–21): NI Multisim simulation of a wearable stress‑meter with sensor‑fusion analytics.

Real-time emotion recognition with 40-channel EEG, facial analysis & PPG fusion - PyQt6 interface with DEAP dataset, KNN/SVM classifiers

Implementation of the core Adaptive Chirplet Transform (ACT) algorithm using THRML (Thermodynamic sampling) to efficiently find the best matching atom in the continuous space

stema de Aquisição e Classificação de Sinais EMG para Controle de Próteses Mioelétricas. Projeto baseado em Arduino e machine learning para leitura, processamento e identificação de movimentos musculares via sensores EMG, com foco em aplicações assistivas e controle de próteses de mão.

This project demonstrates how electromyographic (EMG) signals can be used to control a prosthetic hand. The software acquires raw EMG data from surface electrodes, processes it in real time using digital filters, and translates the extracted muscle activity into control commands for servo motors.

Stress detection using physiological signals from the WESAD dataset. Built with Python for time series analysis, feature extraction, and machine learning. Ideal for health tech and wearable applications.

This repository implements a fusion algorithm based on a constant velocity model to improve the accuracy of saccade parameter measurements using electrooculography (EOG) signals. By combining regression-based and threshold-based estimations, the method enhances the detection of saccade amplitude, velocity, and duration.