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Neural Signal Analysis

Module code: MEP55B21 · Semester: Semester 1

Resources​

How it's assessed​

Exam: 50% of the module. TCD doesn't list past papers for it.

Mathematical tools for analysing neural signals (EEG, MEG, fMRI and intracranial data), such as filtering, harmonic analysis, ICA and wavelets, applied to real data sets in MATLAB.

AssessmentWeight
Individual MATLAB assignments50%
Final exam50%
  • You may be returned as non-satisfactory if you attend less than 80% of lectures.

Summarised from the TCD module descriptor, checked 2026-11-09. Details can change from year to year, so check this year's descriptor and Blackboard.

From students who've done it​

No write-ups yet.

Questions by Year​

No past papers for this module are listed on TCD’s past-papers pages. See How it's assessed above.

Topics

  • LTI systems applied to electrophysiology LO2
  • Noise and filtering of electrophysiological data LO2, LO4
  • EEG: generators, acquisition, ERPs, time-frequency analysis LO1, LO3–4
  • Harmonic analysis, independent component analysis, wavelets LO2, LO4
  • Hyperscanning and multi-brain methods LO3, LO6
  • fMRI: origin of the BOLD signal and its analysis LO1, LO5
  • Neural connectivity and computational modelling LO6
Learning outcomes (exam covers LO1–6)
  1. Describe where neural signals come from and how they are imaged
  2. Apply quantitative methods to dynamic, multivariate neural data
  3. Design EEG experiments with reliable stimuli, good signal quality and ethics
  4. Build pipelines to analyse EEG and event-related potentials with time-frequency methods
  5. Analyse and interpret structural and functional MRI data
  6. Evaluate and apply connectivity and modelling frameworks
  7. Communicate methods, results and interpretation

Summarised from the module descriptor, checked November 2026.