A partir de hoy lunes 28 de septiembre a las 4 pm y hasta el 4 de octubre se encuentra abierto el plazo de solicitud del curso online «International Doctoral School on Multivariate Pattern Analyses of Electroencephalography data: Theory and Practice»
El curso está dirigido a estudiantes con conocimiento de la técnica de electroencefalografía (EEG) que tengan interés en aprender a aplicar análisis de patrones a estos datos. Para poder seguir el curso, es necesario un conocimiento al menos básico de Matlab y de la señal de EEG. El material quedará disponible en la web de forma permanente, y la docencia online tendrá lugar la semana del 23 al 27 de noviembre de 2020.
La información del curso y formulario de solicitud se encuentran disponibles en su web https://wpd.ugr.es/~multiveeg/
Página web del curso en la EIP: https://escuelaposgrado.ugr.es/doctorado/escuelas/escuelasdeverano/2020/idss-2020/electroencephal_2020/index
Email de contacto: mruz@ugr.es (María Ruz, http://www.ugr.es/~mruz/, CIMCYC).
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Multivariate pattern analyses of Electroencephalography data – Theory and Practice – https://wpd.ugr.es/~multiveeg/
The course will combine pre-recorded theoretical contents, which will be available in advance, with synchronous practice live sessions in smaller groups. It will be offered for free to 30 selected 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 main contents are the following:
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.
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.
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.
Introduction to Representational Similarity Analysis (RSA)
- Practice with RSA in a simple dataset.
- State segmentation methods.
- Beyond multivariate analyses: Forward encoding models.
- Practice with building a simple forward encoding model.
Introduction to Deep Neural Networks (DNN)
- Practice with simple DNN.
- Extracting features.
- RSA and forward encoding.
