23-27th of November 2020

[MULTIVARIATE EEG ANALYSIS] _online course

Multivariate pattern analyses of Electroencephalography data - Theory and Practice.

The course will combine pre-recorded theoretical contents, which will be available in advance, with synchronous practice live sessions in smaller groups.

General information_

23-27 nov.

dates

0
number of students

free

fee

Course description_

The course will be offered for free to the accepted students, which will have the opportunity to participate in practical sessions with real data and interact with the course instructors. The contents, however, will be available on the website for anyone aiming to learn multivariate methods applied to EEG data.

The course will combine pre-recorded theoretical contents, which will be available in advance, with synchronous practice live sessions in smaller groups.

Main contents_

day_1

Introduction to the electroencephalography (EEG) signal

  • Biological basis and properties.
  • EEG oscillations.
  • Signal preprocessing for multivariate analyses.
  • Considerations for experimental designs.
  • Pros and cons of Univariate vs. Multivariate approaches.

day_2

Introduction to classifiers

  • Families and properties.
  • Time-resolved decoding.
  • EEG features for classifier training.
  • Practice with EEG preprocessing and classification (LDA).
  • K-fold cross-validation.

day_3

Generalized classification

  • Searchlight analysis.
  • Practice with time generalization.
  • Evaluation of the performance of classifiers.
  • Controlling methodological and analytical confounds.
  • Practice with classifier interpretation and addressing confounds.

day_4

Introduction to Representational Similarity Analysis (RSA)

  • Practice with RSA in a simple dataset.
  • State segmentation methods.
  • Beyond multivariate analyses: An introduction to forward encoding models.
  • Practice with building a simple forward encoding model.

day_5

Introduction to Deep Neural Networks (DNN)

  • Practice with simple DNN.
  • Extracting features.
  • RSA and forward encoding.