Teaching Material


Lau Møller Andersen - PhD


Contact details

Email: lau.moller.andersen[at]ki.se

Phone: +46 8 524 832 09

Visiting address:

NatMEG, Nobels väg 9,

171 77 Stockholm, Sweden

TEACHING MATERIAL


Teaching material on how to do MEG analysis on open datasets


Seminar at KIBIN (KI Brain Imaging Community)


 

Purpose and Aims


The main purpose is to introduce attendees to the practicalities of doing MEG research with a specific focus on pipelines for group level analysis and statistics

 

Slides


PDF

 

Teaching Material for course 3035: Imaging in neuroscience with a focuse on MEG and EEG methods


PhD-Course run on February 5th-9th 2018 at Karolinska Institutet, Sweden


 

Purpose and Aims


The main purpose of the course was to provide the students with a solid understanding of the tools available to analyze brain activity data measured with magnetoencephalography (MEG) and electroencephalography (EEG). The students should develop the ability to critically review results provided by different methods, to select the most adequate tools and experimental designs to answer different questions and to compare their relative advantages.

 

Slides


Introduction to MEEG

These slides cover:

Origin of signal

Recording of data

Preprocessing

Maxwell filtering

High- and lowpass filtering

Demeaning

Re-referencing

Independent Component Analysis

Epochs and Evoked Responses

In-phase versus out-of-phase

Source Reconstruction

Source Model

Volume Conductor

Forward Model

Sensor Sensitivity

Co-registration

Noise Covariance

Dipole Fits

Minimum Norm Estimate

 

Group Analysis MEEG

These slides cover:

Pipelines for doing Group Analysis

Grand Averages

Morphing individual anatomies onto Template anatomies

Statistics

t-statistics

Permutation Testing

 

 

Teaching Material for course: Psychophysiology HT 2018


Master's course class run on September 18th 2018 at Stockholm University, Sweden


 

Purpose and Aims


Teaching students the basics of MEG

 

Slides


Introduction to MEG

These slides cover:

Origin of Signal

Recording of data

Preprocessing

Filtering

Independent Component Analysis

Epochs and Evoked Responses

In-phase versus out-of-phase

Source Reconstruction

Source Model

Volume Conductor

Forward Model

Dipole Fits

Minimum-Norm Estimate

On-scalp MEG

 

Teaching Material for course: Studium Generale


Bachelor's course class run on March 19th 2015 at Aarhus University, Denmark


 

Purpose and Aims


Introducing students to explanations, complexity and parsimony. Taught in Danish

 

Slides


 

Explanations and complexity (in Danish)

These slides cover:

The concept of explanatory power

The concept of parsimony in science (Occam's Razor)

The concept of inference to the best explanation

 

A short introduction to R


Introduction given to R at Aarhus University March 30th 2015

Slides

 

Rant about the Oxford Comma


 

The Oxford does not disambiguate: Click here for PDF

Minimum Norm Estimate

Figure from:

Andersen, L. M. Group Analysis in MNE-Python of Evoked Responses from a Tactile Stimulation Paradigm: A Pipeline for Reproducibility at Every Step of Processing, Going from Individual Sensor Space Representations to an across-Group Source Space Representation. Front. Neurosci. 12, (2018).

On-scalp MEG

See publication

Andersen, L. M. et al. Similarities and differences between on-scalp and conventional in-helmet magnetoencephalography recordings. PLOS ONE 12, e0178602 (2017).

Beamformer

See workshops page

 

© Lau Møller Andersen 2018. All rights reserved