Neurotechnology

Issue

Neurotechnology: neuronal motor prosthesis / brain-machine interface (BMI)

Personal Data

Dr. Carsten Mehring, Dr. Jörn Rickert, Dr. Tonio Ball

Department

Bernstein Center for Computational Neuroscience (BCCN) together with the Instituteof Biology I, the Institute for Microsystem Technology (IMTEK) and the Epilepsy Center of the University Medical Center Freiburg

Know-How

• Analysis and interpretation of neuronal signals
• neuronal decoding
• adaptive algorithms for decoding brain-signals
• brain-machine interface technology
• models for human motor learning
• invasive and non-invasive electrophysiology of the human brain
• functional neuroanatomy of the cortical motor system
• functional neuroimaging

Experience

• Analysis of all potential signal types for brain-machine interfacesAdvanced mathematical algorithms for interpreting neuronal data
• Real-time software-solutions for processing and interpretation of neuronal data
• Invasive and non-invasive electrophysiology with humans
• Brain-computer  interface experiments with humans

Publications

1. Mehring C, Nawrot MP, Cardoso de Oliveira S, Vaadia E, Schulze-Bonhage A, Aertsen A, Ball T (2005) Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex. Journal of Physiology – Paris Vol.98: 498-506.
2. Rickert J, Cardoso de Oliveira S, Vaadia E, Aertsen A, Rotter S, Mehring C (2005) Encoding of movement direction in different frequency ranges of motor cortical local field potentials. Journal of Neuroscience Sep 28;25(39):8815-24.
3. Mehring C, Rickert J, Vaadia E, Cardoso de Oliveira S, Aertsen A, Rotter S (2003) Inference of hand movements from local field potentials in monkey motor cortex. Nature Neuroscience 6(12): 1253-1254.
4. Ball T, Schreiber A, Feige B, Wagner M, Lücking CH, Kristeva-Feige R. The role of higher-order motor areas in voluntary movement as revealed by high-resolution EEG and fMRI, Neuroimage. 1999 Dec;10(6):682-94.

Projects

• Understanding how the brain learns and controls voluntary movements
• Development of a brain-machine interface for movement restoration in severely paralyzed people
• Development of flexible real-time software for decoding brain signals
• Functional neuroimaging of the human motor system in the context of BMI-research