Projects

WIRELESS MOUSE EEG DEVICE

Electroencephalogram (EEG) has been widely used in studies using rodent models to understand brain functions and neurological disorders. However, conventional EEG setups have limits as recording devices are bulky and tethered, causing discomfort and deviating from natural habitat conditions. To address these, we develop an innovative wireless EEG device for mice with a compact size, wireless recording via Bluetooth, and minimal invasiveness through screw mounting. The device enhances experimental setups while ensuring the well-being of the mice in a more realistic environment facilitating easier translation to human studies. In an experimental setting involving induced seizures, EEG signals were recorded and analyzed to discern differences across epilepsy phases in both anesthetized and awake stages. This device holds promise to revolutionize EEG research using mice, bridging the gap between laboratory conditions and real-world scenarios, thus advancing our understanding of neurological phenomena.

WIRELESS PH SENSING INSIDE SMALL TUBES

A miniaturized wireless data acquisition system was developed to enable pH measurement inside a test tube. The system, designed for wireless power and data transmission via Bluetooth Low Energy (BLE), includes a power and communication circuit and coil fitted into the cap of a 50-mL test tube. The power management system comprises a receiver coil with inductance, a wireless charger, and a small Li-ion rechargeable battery. Energy is received from a transmitter coil on the test-tube rack, and the sensor measures pH by correlating potential differences between working and reference electrodes to a calibration standard. A low-power instrumentation amplifier and analog-to-digital converter (ADC) process and digitize potential signals. And the data are transmitted wirelessly using BLE, the system allows remote monitoring on an smartphone.

EEG-BASED BIOMETRIC IDENTIFICATION USING A REAL-TIME RASPBERRY PI-BASED SYSTEM

Despite the growing interest in the use of electroencephalogram (EEG) signals as a potential biometric for subject identification and the recent advances in the use of deep learning (DL) models to study neurological signals, such as electrocardiogram (ECG), electroencephalogram (EEG), electroretinogram (ERG), and electromyogram (EMG), there has been a lack of exploration in the use of state-of-the-art DL models for EEG-based subject identification tasks owing to the high variability in EEG features across sessions for an individual subject. In this paper, we explore the use of state-of-the-art DL models such as ResNet, Inception, and EEGNet to realize EEG-based biometrics on the BED dataset, which contains EEG recordings from 21 individuals. We obtain promising results with an accuracy of 63.21%, 70.18%, and 86.74% for Resnet, Inception, and EEGNet, respectively, while the previous best effort reported accuracy of 83.51%. We also demonstrate the capabilities of these models to perform EEG biometric tasks in real-time by developing a portable, low-cost, real-time Raspberry Pi-based system that integrates all the necessary steps of subject identification from the acquisition of the EEG signals to the prediction of identity while other existing systems incorporate only parts of the whole system.

BATTERY MANAGEMENT SYSTEM (BMS)

The main objective of the project was to design a robust system for managing the discharge and charging process of different types of batteries with different protection measures. The system can support up to 4.5kW. My role was mainly to validate the power on part of the circuit board. I was also tasked with designing and executing the validation of the complete integration system (HW+SW).

ELECTRIC POWER SYSTEM (EPS)

I carried out a project for the nanosatellite laboratory of the UPC. The intention of the project was to design the power electronics system for a nanosatellite. In this project I did the definition, design and validation of the entire electronic design. The design was composed by different stages: logic and control stage, DC-DC converters and MPPT for photovoltaic cells. After the schematic design phase, I designed the PCB layout for the validation system.

SENSING SYSTEM

During the last years of university, I completed several projects related to sensor systems. One of them was for the company HP, in which, together with a team of 9 engineers, we designed a mass flow measurement system using capacity and pressure sensors. Another relevant project was the design of a weather station with different sensors (temperature, pressure, humidity, etc...).