This course will give students a first introduction to "Cognitive Electrophysiology", in which electrophysiology is used to measure and understand cognitive functions such as visual perception, attention, working memory, and language in terms of brain processes. The course will provide students with a rudimentary theoretical and methodological background in electroencephalography (EEG) and to some extent magnetoencephalography (MEG), enabling them to better understand and interpret currently cutting-edge analysis techniques that are increasingly being applied to EEG, MEG, and other electrophysiological signals in cognitive neuroscience.
Themes that will be covered:
- The neurophysiological basis of EEG and MEG: history, relationship with neural activity, source localization, the inverse problem
- Preprocessing of electrophysiological signals: what is a ‘signal’? re-referencing, filtering, artifact rejection
- Basic analyses: Event Related Potentials (ERPs), the multiple comparison problem
- Important classical findings using ERPs in the context of cognitive functioning: ERP components involved in visual and/or language processing such as the C1, P1, N2, P3, N400, P600; lateralized components involved in action selection, attention and memory such as the LRP, N2Pc, CDA. The functional meaning of ERP components, and how to set up EEG experiment.
- Rudimentary time-frequency analysis: Time-frequency decomposition using fourier and wavelets, relationship between ERPs and the time-frequency domain, total power versus induced power
- Multivariate statistics: brain reading by obtaining classification accuracy through decoding methodology, train-test analysis approaches, investigating cortical stability through temporal generalization matrices.
- Building forward encoding models that specify the relationship between cortical activity and some continuous cognitive variable, allowing one to predict cognitive contents or cortical activations maps for ‘new’ conditions for which no data exists