Objectives of Programme's Learning Outcomes

• Scientific skills
• Implement and independently carry out an experimental approach, validate a model by comparing its predications with experimental results, assess the limitations of the model's validity, and identify sources or error.
• Use the main measuring instruments and identify sources of error.
• Appropriately select measuring instruments, analyse and process the signal obtained
• Be a responsible researcher: - Know how to base their reasoning on data obtained from scientific literature. - Know how to integrate an ethical dimension in their reasoning. - Be loyal (to the facts, to the team, and to intellectual property). - Do not tamper with results. - Do not exploit the work of others. - Demonstrate experimental rigor.
• Professional integration skills
• Make a presentation illustrating arguments adapted to the object, the circumstances and the public: speak in public
• Master scientific English: - Present scientific results, orally and in writing. - Answer scientific questions in English

Learning Outcomes of UE

Training to various brain activity analysis techniques (EEG, eye-tracking, fNIRS, etc.) — with a special focus on EEG.

This learning activity includes:
- Lectures (theory on recording methods, signal processing, and analysis)
- Practical sessions (EEG recording and signal processing in Python)

The “Introduction to Coding” course is a prerequisite.

UE Content: description and pedagogical relevance

Training to various brain activity analysis techniques (EEG, eye-tracking, fNIRS, etc.) — with a special focus on EEG.

This learning activity includes:
- Lectures (theory on recording methods, signal processing, and analysis)
- Practical sessions (EEG recording and signal processing in Python)

The “Introduction to Coding” course is a prerequisite.

Prior Experience

Fundamentals of Python programming (variables, lists, loops, functions, classes).