MSL906C Applying Technologies at the Intersections of Artificial Intelligence and Neuroscience

Course Provider

National Institute of Education (NIE)

Certification

FlexiMasters

Academic Unit

2

Introduction

Magnetic resonance imaging (MRI) hs revolutionized medical diagnoses since the 1970s and cognitive neuroscience research starting in the late 1990s. fMRI measures hemodynamic activity, which correlates, albeit in complex ways, with neural spiking and local field potential fluctuations (Singh 2012). fMRI is therefore taken as an indirect measure of brain activity. fMRI data can be conveniently stored in MATLAB as four-dimensional (4D) matrices, where the fourth dimension is time. When analysing fMRI data, the time course of activity at each voxel is treated as the dependent variable with which to perform statistical analyses.

In this course, participants will learn image segmentation procedures, which is an important skill for most neuroscience applications. Participants will also learn how to build Graphical User Interfaces (GUIs) that utilise the code for fMRI / EEG Analyses.

 

This course is part of:
- Graduate Certificate in Mind, Brain, and Education
- FlexiMasters in Mind, Brain, and Education

Learners need to complete any of the following three sets of courses to attain the Graduate Certificate in Mind, Brain, and Education (12 AU):

  • MSL901A, MSL901B, and MSL901C  OR MSL901
  • MSL904A, MSL904B, and MSL904C  OR MSL904
  • MSL906A, MSL906B, and MSL906C  OR MSL906
  • MSL907A, MSL907B, and MSL907C  OR MSL907

Upon completion of the fourth set of courses, learners will fulfil the AU requirements to attain the FlexiMasters in Mind, Brain, and Education (16 AU).

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In this module, participants will learn the following techniques to analyse neuroscience-related data:
- Magnetic Resonance Images
- fMRI Image Segmentation
- fMRI Image Smoothing and Sharpening
- Linear Methods to Fit Models to EEG Data
- Nonlinear Methods to Fit Models to EEG Data
- Neural and Cognitive Simulations of EEGData
- Classification and Clustering of EEGData
- Building Graphical User Interfaces for fMRI / EEG Analyses

Scope/ Syllabus:

The module will cover the following areas:
• Introduction to Magnetic Resonance Images
• fMRI Image Segmentation
• fMRI Image Smoothing and Sharpening
• Linear Methods to Fit Models to EEG Data
• Nonlinear Methods to Fit Models to EEG Data
• Neural and Cognitive Simulations of EEG Data
• Classification and Clustering of EEG Data
• Building Graphical User Interfaces (GUI) for fMRI / EEGAnalyses

Recommended for teachers or working professionals in adult education sector.

Standard Course Fee: S$2,463.40

  • All fees stated are inclusive of 9% GST.
  • NTU/NIE alumni may utilise their $1,600 Alumni Course Credits. Click here for more information. 

 

COURSE TITLE   ACADEMIC UNIT
MSL901A Understanding Developments in Science of Learning*1
MSL901B Applying Cognitive and Affective Science of Learning*1
MSL901C The ‘science’ of Human Learning*2
MSL904A Foundations of Educational Neuroscience*1
MSL904B Understanding Perspectives in Educational Neuroscience*1
MSL904C Applying Social Neuroscience*2
MSL906A Understanding AI-enabled Computational Neuroscience1
MSL906B Analyses using AI-enabled Computational Neuroscience1
MSL906C Applying Technologies at the Intersections of Artificial Intelligence and Neuroscience2
MSL907A Understanding Translational Research*1
MSL907B Ethical concerns of translating Educational Neuroscience*1
MSL907C Evaluating Science of Learning Translation*2
MSL901 Foundations in Science of Learning*4
MSL904 Educational Neuroscience: Principles, Perspectives, Practices*4
MSL906 Education at the Intersection of Artificial Intelligence and Neuroscience4
MSL907 Translating Educational Neuroscience4

Listed courses are:

  • Credit-bearing and stackable to Graduate Certificate in Mind, Brain, and Education (total 12AUs) and FlexiMasters in Mind, Brain, and Education (total 16AUs)

*SSG funded and SkillsFuture Credit approved

 

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