! POSITION FILLED !
Internship type
Master 2 Engineering (Master 2 or last year of an engineering school in cognitive neuroscience or biomedical engineering or signal processing or machine learning)
Date and duration of the internship
6 months, first semester 2025, from February to July
Context
Motor adaptation is a crucial process in human life, enabling us to adjust our movements based on sensory feedback. For instance, imagine being asked to play your favorite game of petanque with a set of balls that are heavier than usual. You would need several trials to adapt your movements and achieve the same performance. This adaptation relies on the sensory prediction error—the difference between the intended and actual outcomes on each trial. Understanding motor adaptation has broad applications, from rehabilitation in patients after brain or body injuries to optimizing motor skills in sports, work, and robotics (motor adaptation is essential for advanced dexterity).
Despite its ubiquity, motor adaptation is still not fully understood at the neural level. This project aims to bridge the gap between behaviorally-relevant variables, such as the size of errors on a given trial, and neural data obtained from magnetoencephalography (MEG) recordings.
Objective
The objective of this internship is to connect behavioral data, particularly error sizes and adaptation speed during motor tasks, with neural data using advanced machine learning techniques. The intern will contribute to the development of a machine learning pipeline that decodes behavior based on neural data.
Mission
The intern will
- Develop Python code for a machine learning pipeline to process and analyze neural (magnetoencephalography data: 275 channels with a 2kHz sampling rate, recorded during previous experiments) and behavioral (joystick movements) data;
- Utilize state-of-the-art multivariate machine learning methods coming from Brain Computer Interface (BCI) field, specifically Source Power Comodulation (SPoC) to connect neural data with behaviorally-relevant variables. The degree of generalisation of machine learning models between contexts (sub-datasets) will be used as a proxy of how the brain is able to transfer learning from one context to another (similar to different petanque sets’ parameters from the example)
- Gain experience in both applied machine learning and neuroscience research, enhancing their skills and CV for future academic research or ML/AI industry opportunities.
Skills required
Python, machine learning (scikit-learn is enough), basic signal processing. Minimal understanding of non-invasive neurophysiological brain recordings is a big plus, MNE tools is a plus.
Monetary compensation
Gratifications de stage (selon la réglementation en vigueur)
Contact
Dmitrii TODOROV, PhD; Chaire Professeur Junior
Neural Connectivity and Plasticity (NCP) team