The dates of the annual SPM course for MEG/EEG have been announced! Read the message from Hayriye Cagnan below:
The course will present instruction on the analysis of MEG and EEG data. The first two days will combine theoretical presentations with practical demonstrations of the different data analysis methods implemented in SPM. On the last day participants will have the opportunity to work on SPM tutorial data sets under the supervision of the course faculty. We also invite students to bring their own data for analysis.
The course is suitable for both beginners and more advanced users. The topics that will be covered range from pre-processing and statistical analysis to source localization and dynamic causal modelling. The program is listed below.
Available places are limited so please register as early as possible if you would like to attend! For any administrative questions, please contact Ms Kamlyn Ramikssoon (k.ramkissoon@ucl.ac.uk).
Monday May 7th (12 Queen square, 4th floor)
9.00 – 9.30 Registration
9.30 – 9.45 SPM introduction and resources
9.45 – 10.30 What are we measuring with M/EEG?
10.30 – 11.15 Data pre-processing
Coffee
11.45 – 12.30 Data pre-processing – demo
12.30 – 13.15 General linear model and classical inference
Lunch
14.15 – 15.00 Multiple comparisons problem and solutions
15.00 – 15.45 Bayesian inference
Coffee
16.15 – 17.45 Group M/EEG dataset analysis – demo
17.45 – 18.30 Advanced applications of the GLM
Tuesday May 8th (33 Queen square, basement)
9.30 – 10.15 M/EEG source analysis
10.15 – 11.15 M/EEG source analysis – demo
Coffee
11.45 – 12.30 The principles of dynamic causal modelling
12.30 – 13.15 DCM for evoked responses
Lunch
14.15 – 15.00 DCM for steady state responses
15.00 – 15.45 DCM – demo
Coffee
16.15 – 17.00 Bayesian model selection and averaging
17.00 – 18.30 Clinic – questions & answers
19.00 – … Social Event
Wednesday May 9th
9.30 – 17.00
Practical hands-on session will take place in UCL computer classrooms. Participants can either work on SPM tutorial datasets or on their own data with the help of the faculty. There will also be an opportunity to ask questions in small tutorial groups for further discussions on the topics of the lectures.