Loic Lorente Lemoine

Loic Lorente Lemoine

(MPhil) CompSci @
Cardiff University

Hello there! I'm Loic, a Computer Science student at Cardiff University, currently completing my MPhil and working as a research assistant with the Agile CPS Lab.

I earned my BSc in Computer Science from CU in 2024, graduating with First Class Honours after completing my dissertation on EEG-based seizure detection with TinyML. Currently, my master's thesis involves developing a Security Operations Center for vehicle CANs." As such, my research focuses are typically on machine learning for embedded devices and cyber-physical systems.

Loic Lorente Lemoine

Education

2024—Present

Cardiff University

M.Phil. in Computer Science

Advisor: Dr. Amir Javed, Dr. Nick Pham

Thesis: A Vehicle CAN (Controller Area Network) On-Board Security Operations Center (SOC)

2021—2024

Experience

2024—Present

Research Assistant AgileCPS Labs, Cardiff University

Publications

EIFCOM 2024

Stress-GPT: Stress detection with an EEG-based foundation model

Catherine Lloyd, Loic Lorente Lemoine, Reiyan Al-Shaikh, Kim Tien Ly, Hakan Kayan, Charith Perera, Nhat Pham

We fine-tune a large language model for stress detection and evaluate it on a 40-subject open stress dataset. This is followed by additional experiments comparing traditional machine learning methods, with key observations highlighted to guide future research directions.

MOBIUK 2024

Epileptic seizure detection with Tiny Machine Learning - a Preliminary Study

Loic Lemoine, Nhat Pham

CNN models are created with melspectrogram EEG data to detect three different types of common epileptic seizures. These models are compressed and deployed onto an Arduino platform.

Awards

2024 - Cardiff University

Best Final Year Project, BSc Computer Science and Variants

2024 - Global Wales, Cardiff University, and Vietnam National University - Ho Chi Minh University of Technology

First Prize, Student Poster Competition on “AI, Smart Healthcare, and IoT”

2024 - Thales Group and Cardiff University

Thales Group MPhil Scholarship

Portfolio

Epileptic Seizure Detection with Tiny Machine Learning

Epileptic Seizure Detection with Tiny Machine Learning

PythonPyTorchArduinoTinyMLEEG

A study on the use of tiny CNN models to detect three common types of epileptic seizures using melspectrogram EEG data. Quantized and deployed models remain reliable, with tonic-clonic, non-specific, and absence seizures presenting accuracies of 85%, 81%, and 99% respectively. Developed as a part of my undergraduate dissertation.