Seminar: Machine Learning and Artificial Neural Networks in Biomedical Applications

Lecturer Sebastian Nagel
Group leader / Postdoc
Lab: Neural Interfaces and Brain Signal Decoding
Sebastian Nagel

Instructors Christian Niethammer
PhD student
Lab: Neural Interfaces and Brain Signal Decoding
Christian Niethammer

Alexander Blöck
PhD student
Lab: Neural Interfaces and Brain Signal Decoding
Alexander Blöck

Katrin Sippel
Post-Doc
Lab: Neural Interfaces and Brain Signal Decoding
Katrin Sippel

Tutorial 06.11.2020 10:00 c.t.
Amount 2 SWS / 3 LP
Type of course Seminar
Entry in course catalog Alma

Due to the current situation, the seminar will be held via Zoom video conference. All slots are already taken.

Description

The Seminar “Machine Learning and Artificial Neural Networks in Biomedical Applications” covers current topics of signal processing on neural signals (e.g. fMRI, EEG or MEG) for their use in biomedical applications (e.g. neuroprosthetics or brain-computer interfaces, BCIs) and related topics; as well as methods and algorithms applied in those fields.

Learning Objective

  • Transfer of factual knowledge.
  • Search scientific literature on specific topics.
  • Familiarize yourself with the terminology of a subject area.
  • Prepare a well structured and informative presentation.
  • Present this lecture safely and in a way that is interesting for the audience.
  • Keep to a tightly limited time frame for a presentation.
  • Give and receive constructive criticism on a presentation style.
  • Write a scientific paper
  • Important for the seminar: understanding the content and passing it on to the audience.

Topics

You can either work on a specific biomedical topic or on a specific machine learning method. Here are some examples:

Biomedical topics

  • Error Potentials
  • Motor Imagery
  • P300
  • Visual evoked potentials (cVEP, SSVEP, MVEP, …)
  • Brain-Computer Interfaces
  • Fetal Magnetoencephalography

Machine learning methods

  • Support Vector Machine
  • Ridge Regression
  • Convolution Neural Networks
  • Random Forests

Presentation duration and topic structure.

Ideally, the presentation should last exactly 20 minutes, no longer. This speaking time is followed by a short discussion on both the content and the style of the presentation, so that a total of 30 minutes is available for each contribution. In order for a lecture to be informative and interesting for the rest of the audience, it has to have a certain form. Each participant should therefore contact his/her supervisor at an early stage.

There will be regular meetings with the supervisor to discuss current activities.

Milestones

  • November 22nd: choose your topic and find scientific papers
  • January 17th: hand in written report (mandatory date!)
  • January 31st: hand in feedback for other student’s report
  • Beginning of February: rehearsal talk (optional)
  • February 12th/19th: final talks
  • February 21st: hand in final written report

Comment

We’d welcome if you pre-register for this course. Please send a mail with your name, student id (“Matrikelnummer”), branch of study (CS / bioinformatics / …) and how far you’ve progressed in your studies to Sebastian Nagel
Group leader / Postdoc
Lab: Neural Interfaces and Brain Signal Decoding
Sebastian Nagel
.

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