Department of Neurotechnology
Faculty of Medicine
University Hospital Knappschaftskrankenhaus Bochum
In der Schornau 23-25
44892 Bochum
Phone:
+49 176-43697855
Email:
christian.klaes@ruhr-uni-bochum.de
In our research group, we are developing advanced neuroprostheses and other assistive devices to help people with severe paralysis live more independently. To achieve this goal, we use a highly interdisciplinary approach involving neuroscience, neurosurgery, machine learning, and virtual reality (VR).
One approach, for example, is the design of a so-called "brain-computer interface" (BCI). A BCI is a system that enables direct communication between the brain and computers and can bridge a damaged spine, among other things. Neuronal signals are "read" from the brain and the patient's movement intentions are decoded with the help of special software. These decoded movement intentions are then used to control machines, prostheses or exoskeletons. This will enable paralyzed patients to regain independence and thus significantly improve their own quality of life.
To achieve this goal, however, we need to better understand how the brain processes sensory input and motor output to generate movement intentions. But how does the brain learn to control a BCI system? How do individual neurons become selective when learning new tasks? And how does the brain make the decision to select a particular object from a set of many?
However, neurorehabilitation can also be achieved through VR, for example. For this, we use high-resolution VR goggles and controllers to put healthy people into environments that would be impossible or difficult to implement in reality. In a recently published study, we were able to show that we can simulate neuroprosthesis control problems in VR with healthy subjects to better understand where control difficulties may arise.
Many of our problems are very complex and require the use of machine learning - or more generally AI systems. We are doing intensive research on applications of AI in medical and rehabilitation contexts. In the future, hybrid systems and the analysis of large amounts of medical data will gain importance. We want to make an important contribution here and are working with many collaborators to help shape the future of neurotechnology in Germany.
Ali, O., Saif-Ur-Rehman, M., Dyck, S., Glasmachers, T., Iossifidis, I., & Klaes, C. (2022). Enhancing the decoding accuracy of EEG signals by the introduction of anchored-STFT and adversarial data augmentation method. Scientific Reports, 12(1), 4245. https://doi.org/10.1038/s41598-022-07992-w
Lienkämper, R., Dyck, S., Saif-Ur-Rehman, M., Metzler, M., Ali, O., & Klaes, C. (2021). Quantifying the alignment error and the effect of incomplete somatosensory feedback on motor performance in a virtual brain-computer-interface setup. Scientific Reports, 11(1), 4614. https://doi.org/10.1038/s41598-021-84288-5
Saif-Ur-Rehman, M., Ali, O., Dyck, S., Lienkämper, R., Metzler, M., Parpaley, Y., Wellmer, J., Liu, C., Lee, B., Kellis, S., Andersen, R., Iossifidis, I., Glasmachers, T., & Klaes, C. (2021). Spikedeep-classifier: A deep-learning based fully automatic offline spike sorting algorithm. Journal of Neural Engineering, 18(1). https://doi.org/10.1088/1741-2552/abc8d4
Saif-Ur-Rehman, M., Lienkämper, R., Parpaley, Y., Wellmer, J., Liu, C., Lee, B., Kellis, S., Andersen, R., Iossifidis, I., Glasmachers, T., & Klaes, C. (2019). Spikedeeptector: A deep-learning based method for detection of neural spiking activity. Journal of Neural Engineering, 16(5), 56003. https://doi.org/10.1088/1741-2552/ab1e63
Aflalo, T., Kellis, S., Klaes, C., Lee, B., Shi, Y., Pejsa, K., Shanfield, K., Hayes-Jackson, S., Aisen, M., Heck, C., Liu, C., & Andersen, R. A. (2015). Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science (New York, N.Y.), 348(6237), 906–910. https://doi.org/10.1126/science.aaa5417
Klaes, C., Kellis, S., Aflalo, T., Lee, B., Pejsa, K., Shanfield, K., Hayes-Jackson, S., Aisen, M., Heck, C., Liu, C., & Andersen, R. A. (2015). Hand Shape Representations in the Human Posterior Parietal Cortex. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 35(46), 15466–15476. https://doi.org/10.1523/JNEUROSCI.2747-15.2015
Andersen, R. A., Kellis, S., Klaes, C., & Aflalo, T. (2014). Toward more versatile and intuitive cortical brain-machine interfaces. Current Biology : CB, 24(18), R885-R897. https://doi.org/10.1016/j.cub.2014.07.068
Klaes, C., Shi, Y., Kellis, S., Minxha, J., Revechkis, B., & Andersen, R. A. (2014). A cognitive neuroprosthetic that uses cortical stimulation for somatosensory feedback. Journal of Neural Engineering, 11(5), 56024. https://doi.org/10.1088/1741-2560/11/5/056024
Klaes, C., Westendorff, S., Chakrabarti, S., & Gail, A. (2011). Choosing goals, not rules: Deciding among rule-based action plans. Neuron, 70(3), 536–548. https://doi.org/10.1016/j.neuron.2011.02.053
Gail, A., Klaes, C., & Westendorff, S. (2009). Implementation of spatial transformation rules for goal-directed reaching via gain modulation in monkey parietal and premotor cortex. The Journal of Neuroscience : The Official Journal of the Society for Neuroscience, 29(30), 9490–9499. https://doi.org/10.1523/JNEUROSCI.1095-09.2009
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Last update: Feb 07, 2024
