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Applied Neuroscience: EEG Fundamentals

Department of Psych. CITY College, University of York

Host Mentor:

Dr. Manousos Klados
Associate Professor, Department of Psychology
Director, MSc/MA in Cognitive/Clinical Neuropsychology
CITY College, University of York Europe Campus
Editor-in-Chief, Brain Organoid and System Neuroscience Journal

This 12-week online fellowship led by Dr. Manousos Klados provides an in-depth introduction to the principles and practices of EEG in applied neuroscience. Designed for early-career researchers, clinicians, and PhD students, the program combines theoretical instruction with hands-on application in EEG signal processing, brain connectivity analysis, and neural network modeling.

Participants will receive a guided research project and meet with Dr. Klados every 2–4 weeks for mentorship, progress tracking, and advanced discussions. The program follows a group-based structure (maximum of 4 participants per class), with live interactive sessions, and runs twice a year (May & October sessions).

Participants are also encouraged to bring their own datasets or research problems and apply the learned techniques in real time to generate meaningful results under expert guidance.

Main Areas of Interest

Neurophysiological basis of cognition and emotion
Mathematical anxiety and its neural correlates
EEG-based biomarkers of mental health
Personality neuroscience
Translational applications of brain networks in clinical populations

Specialties

Neurosciences

Computational Biology

Techniques You Will Learn

EEG data acquisition and preprocessing (including ICA and artifact correction)
Experimental design using PsychoPy
ERP analysis (P300, N400, N170)
Time-frequency decomposition (FFT, Wavelet, ERSP)
Source localization using inverse modeling (LORETA, sLORETA, Beamforming)
Functional connectivity metrics (PLV, coherence, Granger causality)
Graph theory metrics (clustering coefficient, centrality, path length)
Use of tools such as MATLAB, EEGLAB, and MNE

Modules

Prerequisites:

Basic expertise in MATLAB
Background in neuroscience, psychology, cognitive science, or biomedical engineering

Format:

Fully Online
Duration: 12 Weeks
Meetings: Every 2–4 Weeks (Mentor-Guided)
Self-paced modules with additional readings and practice exercises

Week 1: Introduction to Neuroscientific Research and Experimental Design

Learning Objectives:

Understand foundational principles of neuroscientific experimental design
Explore EEG research paradigms and common research questions
Identify variables, controls, and hypotheses

Learning Outcomes:

Explain core components of a neuroscientific experiment
Design a basic EEG-based research question with measurable outcomes

Week 2: Introduction to PsychoPy for Neuroscientific Protocols

Learning Objectives:

Learn the basics of PsychoPy and its role in experiment design
Explore the Builder and Code components
Understand timing precision and stimulus presentation

Learning Outcomes:

Create and customize a simple cognitive task using PsychoPy

Week 3: EEG Basics and Best Practices

Learning Objectives:

Understand the physiological origins of EEG signals
Review EEG systems, electrodes, and setup
Learn artifact minimization strategies

Learning Outcomes:

Identify EEG frequency bands and their cognitive relevance
Implement participant prep and artifact reduction techniques

Week 5: Introduction to Biomedical Signal Processing for EEG

Learning Objectives:

Understand filtering, referencing, and segmentation
Explore temporal and spectral domains

Learning Outcomes:

Apply band-pass and notch filters
Segment EEG data for analysis

Week 6: EEG Preprocessing and Artifact Correction

Learning Objectives:

Apply Independent Component Analysis (ICA) and ASR methods
Review EEG preprocessing pipelines (e.g., EEGLAB, MNE)

Learning Outcomes:

Implement a preprocessing pipeline for ERP studies
Identify and remove major EEG artifacts

Week 7: Event-Related Potentials (ERP): Theory and Measurement

Learning Objectives:

Understand ERP components and cognitive correlates
Learn ERP extraction and analysis techniques

Learning Outcomes:

Compute ERP waveforms from processed data
Identify ERP markers like P300, N400, N170

Week 8: Spectral Analysis of EEG – Time-Frequency Decomposition

Learning Objectives:

Learn FFT and wavelet-based decomposition
Analyze oscillatory brain activity and PSD/ERSP

Learning Outcomes:

Perform time-frequency analysis
Interpret EEG responses across frequency bands

Weeks 9–10: Source Localization and Inverse Modeling

Learning Objectives:

Understand EEG source localization challenges
Explore methods like LORETA, sLORETA, and beamforming

Learning Outcomes:

Apply a basic source localization technique
Interpret spatial brain activity

Week 11: Functional Connectivity in EEG

Learning Objectives:

Explore coherence, PLV, and Granger causality
Understand EEG-based brain region interaction

Learning Outcomes:

Calculate functional connectivity metrics
Interpret inter-regional connectivity

Week 12: Brain Network Analysis and Graph Theory Approaches

Learning Objectives:

Model the brain as a complex network
Understand metrics like clustering coefficient, path length, and centrality

Learning Outcomes:

Apply graph theory to functional connectivity data
Analyze and interpret network properties in health and disease

Program Fee


Total Number of Modules	12
Time duration of Each module	30 minutes
Total Program Cost	$495

This program allows Merit Applications. This program allows merit-based applications for virtual and onsite clinical and research programs. If you are successfully awarded under this category, Trialect or the host mentor will cover the tuition fee only. All applications will be evaluated based on merit. Due to the high level of competition, the chances of being selected under the merit category are quite limited.

Who Can Apply

All those interested in this program

Host Details

Host Name: Dr. Manousos Klados, MSc, PhD. PGCert. FHEA, FIMA

Affiliation: Department of Psych. CITY College, University of York

Address: Europe Campus 24 Pr. Koromila st., 546 22, Thessaloniki, Greece

Website URL: www.mklados.com

Disclaimer:It is mandatory that all applicants carry workplace liability insurance, e.g., https://www.protrip-world-liability.com (Erasmus students use this package and typically costs around 5 € per month - please check) in addition to health insurance when you join any of the onsite Trialect partnered fellowships.

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