[PhD] Emotion classification from EEG signals, ICube & Cephalgo (Strasbourg)
lam...@unistra.fr
We are looking for a candidate for a PhD thesis in the field of emotion recognition from electroencephalographic (EEG) signals. The objective of the thesis is to perform statistical analysis of EEG signals and to find correlations between quantities extracted from the raw signals and the emotions associated with the signals during their recording. The candidate will first acquire EEG signals from several users to build a study database. The thesis will then have two stages:
1. As a comparison method, a ‘classical’ analysis of the data will be performed. That is to perform any necessary pre-processing (filtering, elimination of bad signals, estimation of the quality of the signals and decomposition into brain waves, i.e. alpha, beta, theta, gamma waves...), make a statistical study on the data (extraction of quantities from the processed signals, i.e. Hjorth parameters, spectral entropy, moments...), and to find correlations between these different quantities and the emotions associated with the signals.
2. This will lead to the development of machine learning ( particularly deep learning) algorithms to associate EEG signals with their corresponding emotions based on state-of-the-art models (transformers, convolutional neural networks, etc).
The thesis will be carried out in partnership between the company CEPHALGO (specialised in the development of hardware for the recording of EEG signals and their statistical study) under the supervision of Dr Jonathan Chardin and the ICube research laboratory under the supervision of Dr Thomas Lampert HDR, Chair of Data Science and AI (specialised in the development of machine learning and deep learning models). The candidate will share his/her time between the CEPHALGO company and the ICube research laboratory.
Keywords: Deep learning, Affective computing, Valence arousal model, Fourier transform, classifiers, machine learning, PCA, wavelets, statistical analysis.
Skills required:
Master’s degree (M2) in Computer Science or similar with a strong mathematical component
Experience in machine learning projects, preferably in addition to Deep Learning
A solid knowledge of the python programming language and associated libraries (numpy, scipy, matplotlib)
Project management skills when collaborating with other research partners
Good interpersonal skills to interact with medical professionals and patients
Adventurous towards the dynamic startup environment and scientific challenges
Desirable skills:
Good knowledge of signal processing (Fourier transform, wavelet decomposition, spectrograms, etc.)
Experience in working with EEG data or time-series would be a plus but not necessary
Care for patients suffering from mental disorders
About the company CEPHALGO: Created in 2020, CEPHALGO focuses on introducing technological innovations to assist medical professionals to provide better mental health care. Located in Strasbourg, extended beyond Europe, CEPHALGO’s patient monitoring technique using EEG and AI has been applied in psychiatry across Netherlands, Italy, Spain, Norway, Turkey, and Columbia. Further information can be found at https://cephalgo.com.
About the ICube laboratory: Created in 2013 (from the previous LSIIT laboratory), ICube brings together researchers of the University of Strasbourg, CNRS (French National Center for Scientific Research), ENGEES and INSA of Strasbourg in the fields of engineering and computer science, with imaging as the unifying theme. Further information can be found at https://icube.unistra.fr/en/.
Salary: Negotiable depending on the candidate’s profile. The offer of a thesis contract will be financed via the Cifre programme (Convention industrielle de formation par la recherche: https://www.anrt.asso.fr/fr/le-dispositif-cifre-7844).
Location: The candidate will have to carry out his/her thesis in France between the company's sites and the ICube laboratory, both based in Strasbourg.
How to Apply: Please send an email including your CV, motivation letter, and Master’s grades to Jonathan Chardin at the following e-mail address (**remove the numbers**): jon2athan...@cephalgo.com