Numerical Methods for Cognitive Neuroscience

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The details
Colchester Campus
Full Year
Postgraduate: Level 7
Thursday 03 October 2019
Friday 26 June 2020
04 October 2018


Requisites for this module



Key module for


Module description

The aim of this course is for the student
1) to learn advanced mathematical techniques underlying common psychophysiological analyses.
2) to learn the practical skills required to use and interpret software designed for psychophysiological statistical analyses.
3) to be aware of the variety of the mathematical approaches current within cognitive neuroscience.

At the end of this course students will be able to:
(a) understand the statistical underpinning of a variety of analytical techniques.
(b) interpret the output of several statistical analysis tools.
(b) use commonly available software to analyse raw psychophysiological datasets to reach statistical inferences.

Module aims

No information available.

Module learning outcomes

No information available.

Module information

PS930 has two distinct phases. In the first phase, led by Dr Nicolas
Geeraert, you will be provided with a sound statistical foundation
(alongside students in PS910 & PS914). Then in the second phase, taught
by Dr Steffan Kennett, this foundation will be built upon as the focus
moves to key numerical techniques used by Cognitive Neuroscientists.
The main thrust of the second phase is to give you conceptual knowledge
about the maths that supports the analyses found within published
papers in Cognitive Neuroscience. Alongside these academic
considerations, you will also learn how to use analysis software for fMRI
and EEG analysis.

Coursework test: Students will be tested on the statistical foundation taught in weeks 2-6
Practical test: Students will analyse a raw EEG dataset using appropriate software. They
will derive an estimate of the locations of the sources of
electrophysiological activity. This 2-hour test will take place in room 1.703 of the Psychology department.
Summer Exam:Students will be tested on their conceptual knowledge about common
numerical methods. Emphasis will be placed on understanding, rather than
performing calculations. Additionally, students will be required to interpret
the output of the world's most common fMRI analysis software.
This 2-hour exam takes place during the University Exam Period.

SK/JP 17/7/17

Learning and teaching methods

Lecture 1: Introduction Students will receive a grounding in key concepts of statistical analysis. Topics covered will include probability distributions, z-score, chi-square, F and t Lecture 2: Effects and Power Effect size, Type I and Type II error. Power in statistical tests. Power as applied to t-tests Lecture 3: ANOVA and power ANOVA; key concepts revised. Power in One-Way ANOVAs. Lecture 4: Contrast Analyses Students will be introduced to this form of analysis. This forms a key component for many functional neuroimaging analyses Lecture 5: Advanced ANOVAs Revision of factorial ANOVA. Between-subjects factorial designs including unequal sample sizes. Repeated measured and mixed design ANOVAs Lecture 6: EEG: Noise reduction and removal & the inverse problem. EEG recordings always include noise. Students will be introduced to several sources of noise (artefacts from blinks, eye-movements, muscle etc.). Several approaches to dealing with these noise sources will be introduced, including data removal or modelling (i.e., blink artefact and HEOG). Source decomposition will also be discussed (independent components analysis and principle components analysis). Source reconstruction and the inverse problem will also be discussed. Lecture 7: EEG source localisation The forward model will be introduced and related to the Inverse Problem. The key principles of the different source localisation approaches will be introduced (e.g, beamforming, minimum norm). Software tools commonly used in source localisation will be introduced. These will include freely available packages (e.g., Fieldtrip, SPM) or commonly used proprietary products (e.g., Neuroscan). Lecture 8: Source localisation from raw data A raw dataset will be introduced. Students will be guided through the steps required to localise the source of a prominent event-related electrophysiological component. The practical steps will be related to the theoretical concepts introduced in previous lectures. Lecture 9: Introducing fMRI Students will be introduced to the concepts which connect fMRI to underlying biology. These include magnetic resonance and the heomodynamic response function. Statistical issues will be discussed, including the multiple comparisons problem. Common solutions to the multiple comparisons problem will be introduced (e.g., Gaussian field theory, Regions of Interest, Bootstrapping). Lecture 10: Practical fMRI analysis session A preprocessed fMRI dataset will be introduced. Students will be guided through the steps necessary to analyse appropriately the dataset addressing a series of typical hypotheses. The results of these analyses will be presented appropriately and the meaning behind these results will be discussed.


This module does not appear to have a published bibliography.

Overall assessment

Coursework Exam
65% 35%


Coursework Exam
65% 35%
Module supervisor and teaching staff
Steffan Kennett, Nicolas Geeraert



External examiner

No external examiner information available for this module.
Available via Moodle
Of 34 hours, 0 (0%) hours available to students:
34 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information

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