Alexander Blöck

Photo of Blöck, Alexander

Alexander Blöck
University of Tübingen
Department of Computer Science
Computer Engineering
Sand 13
72076 Tübingen
Germany

Lab
Neural Interfaces and Brain Signal Decoding
Role
PhD student
Telephone
+49 - (0) 70 71 - 29 - 70490
Telefax
+49 - (0) 70 71 - 29 - 50 62
E-Mail
Mail
Office
Sand 14, C203
Office hours
By appointment

Profile on ResearchGate

Research Interests

  • Brain-Computer Interface (BCI)
  • Analysis of EEG data
  • Machine Learning
  • Neural Networks

Research projects

Brain-Computer Interface for home-use application

Home-use BCI Logo

There are several diseases, such as amyotrophic lateral sclerosis (ALS), which can lead to a loss of the ability to communicate. As a healthy person one cannot imagine what it feels like to be trapped in one’s own body - mentally present, but unable to communicate with relatives, this is called locked-in syndrome. However, a distinction must be made between locked-in and complete locked-in syndrome (CLIS). For the former, those affected can still voluntarily control certain muscles, above all the eye muscles, which in turn can be used for communication. Brain-computer interfaces (BCIs), i.e. systems that allow to control a computer by pure brain activity, have proven to be a helpful method for restoring the ability to communicate. However, all recent BCI systems are almost exclusively used in research, since all previous methods are not suitable for real-world applications. This is mainly due to the fact that for a meaningful and independent use, the recognition of the user’s intention (to control the system or not) must be highly accurate. Otherwise, this leads to random classifications/commands, which can be dangerous depending on the application, for example when controlling an electric wheelchair.

Teaching

Seminar: Machine Learning and Artificial Neural Networks in Biomedical Applications Winter 2018 Summer 2020 Winter 2020