Training courses

Ongoing courses

Training session : Multipatient Intracerebral data Analysis (MIA)

This course will be dedicated to the time-frequency analysis of intracerebral signals (including high gamma activity) with the MIA software. It includes one lecture and two practical sessions (hands-on).

  1. MIA : Context, general principle
  2. Hands-on 1 : Single patient analysis (Database explorer, sanity check, Time-Frequency extraction, statistics)
  3. Hands-on 2 : Group analysis (ROI browser, ROI visualization)

General information

Where: La Timone

When : April 2018

Instructors : Anne-Sophie Dubarry (LPL, CNRS, Aix-Marseille Université

Audience : Users interested in analyzing intracerebral recordings using MIA – (Multipatient Intracererbal data Analysis). Some experience in processing electrophysiological/SEEG recordings is required. Basic knowledge on time-frequency analysis is recommended. Teaching in French.

Fees : Free.

Requirements : The participants are required to bring a laptop (an external mouse will add to your comfort) with MATLAB INSTALLED. In order to make the session as efficient as possible, we ask all the attendees to download, install and test the software on their laptops prior to the workshop.

2 places left


Formation Python

13 au 16 janvier 2018



- notions de bases du langage (Strings, Tuples, List, Dictionnaire / Structures de controle), fonctions, modules,
- programmation orienté objet (création de class, notions d'héritages et de surcharges),
- la manipulation de fichier (système, csv, bin),
- l'introduction à une bdd type sqlite,
- l'affichage graphique des données (plot),
- la création d'interfaces graphiques (manuellement puis via l'outil Qt),
- les communications via les portcom, les sockets TCP,
- application Web (Flask, Jinja, SQLite) par l'exemple (ex : gestion d’un parc instrumental)

L’ensemble de ces points feront l’objet de présentations et de séances pratiques.

Eric Duvieilbourg, Ingénieur d’Etudes CNRS au LEMAR ( ), est formateur CNRS. Il a l’expérience de plusieurs de ces formations et a proposé ce programme adapté à nos besoins.

Past courses

The ILCB organizes a 3-days tutorial on the frequentist statistics and advanced issues related to linear mixed models, given by Professor Shravan Vasishth. The tutorial will be interactive and give the occasion to follow a course, but also to practice around hands-on and discussions of specific problems.

Morning sessions will be devoted to lectures, afternoon sessions to participant's’ presentations of their own data, the problems they are faced with, and solutions that could be relevant. The afternoon sessions will be interactive and prepared in advance with professor Vasishth: participants are encouraged to propose questions, datasets, methods they would like to focus on, starting from their own experience, the problems they have encountered, or the type of analysis they would like to apply.

24 Oct 2017 – Tutorial 1: Introduction to foundations of frequentist statistics Part 1 


I will introduce the foundations of frequentist reasoning: the sampling distribution of the sample mean, Type I, II and Type M,S errors, and simple t-tests.

Background reading:

  1. The contents are available as slides (from the SMLP 2017 summer school at Potsdam):
  2. These slides are also available as lecture notes:
  3. Shravan Vasishth and Bruno Nicenboim. Statistical Methods for Linguistic Research: Foundational Ideas – Part I. Language and Linguistics Compass, 10(8):349-369, 2016.


26 Oct 2017 Tutorial 2: Introduction to foundations of frequentist statistics Part 2

Here, I will cover the basic theory of linear models, leading up to linear mixed models.

Background reading:

  1. Slides from SMLP 2017 summer school:


  1. Complete lecture notes on linear modeling:

  1. Shravan Vasishth and Andrew Gelman. The statistical significance filter leads to overconfident expectations of replicability. In Proceedings of Cognitive Science Conference, London, UK, 2017.


rant on youtube:


31 Oct 2017: Tutorial 3: Some advanced issues related to linear mixed models: model specification, and Bayesian LMMs 

For background reading, see:

  1. Hannes Matuschek, Reinhold Kliegl, Shravan Vasishth, R. Harald Baayen, and Douglas Bates. Balancing Type I Error and Power in Linear Mixed Models. Journal of Memory and Language, 94:305-315, 2017.
  2. Douglas Bates, Reinhold Kliegl, Shravan Vasishth, Harald Baayen. Parsimonious Mixed Models. 2015.

  1. Bruno Nicenboim and Shravan Vasishth. Statistical methods for linguistic research: Foundational Ideas - Part II. Language and Linguistics Compass, 10:591-613, 2016

  1. Tanner Sorensen, Sven Hohenstein, and Shravan Vasishth. Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists. Quantitative Methods for Psychology, 12(3):175-200, 2016.

  1. We taught a full one-day course on Bayesian modeling in Tuebingen recently. All materials, including full lecture notes, are here:

The Frequency Following Response (FFR) is an EEG-based measure of the scalp-recorded brainstem response to speech stimuli. In broad terms, it registers neural phase-locking to time-varying F0 and spectral characteristics of the speech signal, thus providing a robust representation of perceived changes in pitch. During a two-day workshop, the invited speaker, Bharath Chandrasekaran (University of Texas at Austin), will provide participants with the technical and theoretical background necessary to collect and analyze FFR data using on-site EEG capabilities (Biosemi Two). A roundtable discussion on the second day of the workshop will explore how FFR may be used to address the neural basis of intonation and its associated linguistic representations. Dates: 6 – 7 June 2017, 9:30 – 17:30 (detailed program to follow) Location: Salle A003, Laboratoire Parole et Langage, Aix-en-Provence The workshop is open to all researchers, doctoral students, and postdocs from BLRI-affiliated institutions. Registration is free but obligatory by going to this link. Space is limited to 25 participants. Lunch will be provided on both days of the workshop. This workshop, organized by James Sneed German, is co-sponsored by the Brain & Language Research Institute and by a grant from the A*MIDEX Foundation (SIRL, n° ANR-11-IDEX-0001-02).
4 mai 2017 - 9h à 17h
Salle des Voûtes, Fédération de Recherche 3C, 3 place Victor Hugo Case 32, Site St. Charles, 13001 Marseille
Formation Club EEG : Introduction à l'Acquisition EEG

Formation BLRI
Intervenants :
Dufau Stéphane, Dittinger Eva, Denis-Noël Ambre, Melmi Jean-Baptiste, Legou Thierry,
Chanoine Valérie, Bolger Deirdre, Zielinski Christelle.

9h – 9h15 : Arrivée et accueil
9h15 – 10h : Le signal neuro-électrique et son origine
10h – 10h20 : Les bases de l’acquisition EEG
10h20 – 10h45 : Le choix des références en EEG
Petit pause
11h – 11h30 : Eléments importants du design d’un protocole expérimental en EEG
11h30 – 12h : Le déroulement d’une séance d’acquisition en EEG
Pause Déjeuner (12h – 14h)
14h – 16h30 : Séances pratiques dans les box expérimentaux du LPC et LNC avec le système Biosemi
16h30 – 17h : Questions, discussions, synthèse de la journée (Salle des Voûtes)

Analyse de signaux neurophysiologiques Brainstorm-MarsPower

Formation BLRI
Anne-Sophie Dubarry (LPL, CNRS, Aix-Marseille Université)

9h à 18h LPL, Aix-en-Provence, Room A-103 
5 avenue Pasteur, Aix-en-Provence LPL
The participants are required to bring a laptop (an external mouse will add to your comfort). In order to make the session as efficient as possible, we ask all the attendees to download, install and test the software and sample dataset on their laptops prior to the workshop.
9:00-9:30 Onsite assistance in installing the material for the training session 
9:30-10:30 Lecture: Training overview Software structure, typical data workflow 
10:30-10:45 Practice (Brainstorm) Database explorer, Review of continuous EEG recordings Montage editor and management of event markers, Frequency filters

10:45-11:00 Coffee break

11:00-12:30 Practice (Brainstorm) Bad channels management, Artifact detection Averaging, exploring the evoked response

12:30-13:30 Lunch 
13:30-14:30 Lecture: Time Frequency

14:30-15:30 Practice (MarsPower) (If time allows) Time-Frequency decomposition into Brainstorm Database explorer Time-Frequency extraction

15:30-16:00 Coffee break

16:00-17:00 Practice (MarsPower) Statistics Group analysis

17:00-18:00 Analyze your own data

Signal sur graphes;application aux neurosciences / Graph signal processing; application to neuroscience

Sophie Achard GIPSA Grenoble
Training course
9h30 - 17h FRUMAM (3rd floor)
Aix-Marseille Université
The training will take place in the FRUMAM third floor seminary room on the campus of Aix-Marseille University.
Functional brain data are often represented as a network or graph to model the brain regions as nodes and the connections as edges. A graph is an abstract object to represent multivariate data on a simple map of connections. In this tutorial, I will give a short introduction on the modelling of data as a network, with references. In a first part, I will describe precisely how to construct the connectivity networks using brain data recordings. Robustness and reproducibility will be discussed precisely. In a second part, I will describe tools to compare and classify the networks based on statistical tests or learning methods. Finally, I will conclude with a practical example from the clinical data on coma patients. 
Related papers:
S. Achard, R. Salvador, B. Whitcher, J. Suckling, E. Bullmore. A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs. In Journal of Neuroscience, 26(1) pages 63-72, Jan. 2006.
S. Achard, C. Delon-Martin, P. E. Vértes, F. Renard, M. Schenck, F. Schneider, C. Heinrich, S. Kremer, and Edward T. Bullmore. Hubs of brain functional networks are radically reorganized in comatose patients. Proc Natl Acad Sci U.S.A., 109(50):20608-13, 2012.
Programme sur 2 jours 

6 Feb. 2017
09h30 Accueil / Welcome 
10h-12h Cours sur les graphes (définition, exploitation de graphes, à quoi ça sert, comment les comparer et faire de la classification) - Discussion et questions
Lecture on graphs (definition, running, use, how to compare and classify them) - Discussion and questions
12h-13h30 Déjeuner / Lunch
13h30-15h30 TP sur le logiciel R. Travail avec un jeu de données réelles fourni pour la formation (1 groupe de contrôles, 1 groupe de patients) - But: mettre en application les outils vus le matin
Practical session with R software. Work with a real data set provided for the training course (1 control group, 1 patient group) - Objective: implement the tools seen in the morning
15h30-15h45 Pause/Break 
15h45-16h45 Cours sur les statistiques sur les graphes
Course on graph statistics

7 Feb. 2017
9h-11h Cours sur la construction des graphes à partir de séries temporelles (outils mathématiques: ondelettes, corrélation, tests multiples)
Lecture on graph construction from temporal data (mathematical tools: wavelets, correlation, tests)
11h00-11h15 Pause/Break 
11h15-12h15 TP avec R
Practical session with R
12h15-13h45 Déjeuner / Lunch
13h45-14h45 Cours pour lier les deux journées (point sur le seuil choisi pour les matrices de corrélation, enjeu et difficultés, autres outils)
Lecture to make the link between the two days (Point on the threshold chosen for the correlation matrices, stake and difficulties, other tools) 
14h45-15h00 Pause / Break 
15h00-17h00 TP avec R (faire toute la chaine de la construction du graphe jusqu'à l'exploitation statistiques des graphes) - Travail à nouveau avec les mêmes jeux de données que la veille, mais cette fois, les séries temporelles seront mises à disposition.
Practical session with R (Make the whole chain of the construction of the graph until the exploitation graph statistics) - Work again with the same datasets as the day before, but this time the time series will be made available.

Organisateurs : Caroline Chaux et Thierry Legou