Natalie Faber
Research Interests
Brain-Computer Interfaces (BCIs), Paradigm Design for BCIs, Neurofeedback, Co-Adaptive BCIs, Brain-Controlled Rehabilitation using Robots, Brain-Controlled Prosthetics, Machine Learning Approaches for Decoding BCI signals
More Information
Contact Information
Natalie Faber is part of the Cybathlon Team Athena-Minerva since it has been formed in April 2015 and since April 2016 she is coordinating this team. She undertook the signal processing as well as the paradigm design part of the Athena-Minerva Team. In these parts Natalie implemented and evaluated several paradigms as well as some signal processing approaches for preprocessing biosignals. Before her PhD, Natalie completed her Master Degree in Computer Science at the Technische Universtät Darmstadt. Her thesis entitled “Augmented Reality Object Tracking and Tampering" was written under the supervision of Prof. Mühlhäuser.
Research Interests
Brain-Computer Interfaces (BCIs), Paradigm Design for BCIs, Neurofeedback, Co-Adaptive BCIs, Brain-Controlled Rehabilitation using Robots, Brain-Controlled Prosthetics, Machine Learning Approaches for Decoding BCI signals
References
-
- Faber, N.; Friess, T.; Fiebig, K.H.; Hesse, T.; Sharma, D.; Peters, J.; Grosse-Wentrup, M. (2016). Neurofeedback for State of the Art Paradigms in Brain-Computer Interfacing , Cybathlon Symposium.
-
- Friess, T.; Fiebig, K.H.; Sharma, D.; Faber, N.; Hesse, T.; Tanneberg, D.; Peters, J.; Grosse-Wentrup, M. (2016). Personalized Brain-Computer Interfaces for Non-Laboratory Environments, Cybathlon Symposium.
-
- Hesse, T.; Faber, N.; Beckerle, P.; Rinderknecht, S.; Peters, J. (2016). Towards Universal Human-Machine Interfaces: A real-life Brain-Computer Interface Case Study, Users Body Experience and Human-Machine Interfaces in (Assistive) Robotics.