Dates: 13.11 14:30-16:00, 20.11 14:30-16:00, 27.11. 14.30-16:00, 4.12 14:30-16:00, 11.12 14:30-16:30

Work load: 3 CP (onsite + asynchronous exercises, exam: final presentation)

Venue: Building MB (North entrance), Floor 5, Seminar Room, Ruhr University Bochum, Universitätsstraße 150, 44801 Bochum

Teachers: Prof. Dr. Xenia Kobeleva, Dr. Nikolai Syrov, Riccardo Catena, Department of Neurostimulation

Course description:

This interdisciplinary workshop equips students with computational neuroimaging skills, focusing on graph theory-based network analysis and predictive modeling for EEG/fMRI data. Through a blend of theory, hands-on coding, and team-based competition, participants will:

Analyze brain networks using graph theory to uncover connectivity patterns.

Build predictive models via multivariate pattern analysis (MVPA) and machine learning to decode brain states.

Compete in teams by applying these methods to real EEG-fMRI datasets, comparing modality-specific strengths for classification.

Format:

Blended learning: Weekly sessions + ~4 hours of self-paced study (tutorials, datasets).

Hands-on focus: From preprocessing to feature extraction and model evaluation.

Team-based projects: Collaborate to design end-to-end analyses, culminating in comparative presentations and discussions.

Outcomes:

Ideal for students in computer science, cognitive science, data science, or computational neuroscience, and computationally interested students from Medicine.

Course syllabus:

1) Theory: Recap of neuroimaging basics, signal preprocessing and results visualization

2) Practical: fMRI and EEG datasets. Data Structure, Reading, Understanding, and Processing. Basic methodology for time series data analysis.

3) Theory and practical on network/graph theory analysis

4) Theory on predictive models in neuroimaging, multivariate pattern analysis

5) Practical: Feature extraction and prediction of brain states, Multivariate pattern analysis, group presentation

Notably, we will employ blended learning elements, expecting some individual learning with prepared materials (about 4 hours/week).

Prerequisites:

The course welcomes students from different academic tracks and encourages interdisciplinary teamwork. Brain data processing requires interdisciplinary skills. Since the project involves combining expertise from both the neuroscientific and computational domains, but it is not common to be expert in both. This course will help you to attain skills in the missing domain. So, we expect enrolled students to have

• Practical skills in EITHER neuroimaging analysis (EEG or fMRI) OR in programming/machine learning (preferably Python),
• Basic understanding of neuroimaging data such as EEG or MRI (as demonstrated by previous courses or projects or self-study with our materials in advance),
• Knowledge of machine learning is a plus, but not a requirement

If you don’t possess all prerequisites, please state your expertise in an email and we will advise you.

Registration Information:

This is a course with limited spaces with strict selection of participants. We appreciate a registration until 01.11.2026, later registrations will be considered but at a lower priority and only if places are left. For registration (and inquiries), please send us an email with

• Your full name,
• Current field of study + semester (and previous degrees),
• One sentence on your previous knowledge in neuroscience and programming.
• A confirmation that you will attend all sessions.

Please send all the information and inquiries to: computationalneurology@ruhr-uni-bochum.de

Prof. Dr. Xenia Kobeleva Lecturer

mail@computationalneurology.com MB 6/189

Course type

Project seminar

Credits

3

Term

Winter Term 2026/2027