Christian Niethammer

Photo of Niethammer, Christian

Christian Niethammer
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 - 77347
Telefax
+49 - (0) 70 71 - 29 - 50 62
E-Mail
Mail
Office
Sand 14, C222
Office hours
By appointment

Publications on Google Scholar

Profile on ResearchGate

Research Interests

  • Brain-Computer Interface (BCI)
  • Analysis of EEG & EMG data
  • Neurorehabilitation
  • Machine Learning

Research projects

Neuro-muscular modelling and analysis of gait abnormalities in early hereditary spastic paraplegia (HSP)

Hereditary spastic paraplegia (HSP) is a group of hereditary, slowly progressive neurological movement disorders characterized by spastic gait disorder. Degeneration of nerve cells in the spinal cord leads on the one hand to progressive spasticity (pathological increase in muscle tension, hyperreflexia) in certain groups of the leg muscles, while other groups are affected by muscle weakness.

For a more detailed understanding of the progressive nerve degeneration and the associated movement impairments as well as the development of assistance systems such as functional electrostimulation (FES), the early, pre-clinical phase of the disease is of particular interest, when the typical clinical symptoms have not yet become visible.

Context-sensitive neural-controlled hand-exoskeleton for restoration of everyday-capability and autonomy after brain and spinal cord injuries

CONSENS-NHE Logo

The development of robotic systems that interacts with the human nervous system, promise to improve the autonomy, quality of life, and capability of people with disabilities. Brain-Computer Interfaces (BCI) can be used to translate the electrical brain activity into control signals of a robotic exoskeleton. Therefore, it is possible to restore grasping movements of a paralyzed hand by interpreting the neural correlates of the movement. For lack of signal quality, BCI systems based on non-invasive methods, e.g. electroencephalography (EEG), can only be used limited in everyday situations.

In the project CONSENS-NHE we develope a non-invasive and everyday suitable neural-controlled hand-exoskeleton, targeting the compensation of a paralyzed hand, as it can occur after strokes or spinal cord injuries. Within the project the latest methods of machine learning, optical object-recognition, movement analysis, and biological inspired design for robotic systems are combined with neurorehabilitative research. The system will allow people with hand paralysis to grasp and manipulate different objects of everyday life. For direct control of the hand-exoskeleton, the grasping intention is identified based on neural signals measured on the scalp using EEG.

Teaching

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

Publications