Research and Thesis Projects
We want to spark the interest of bachelor and master students in understanding the brain, and particularly the neural control of movement. Major principles of systems neuroscience and motor control can be experienced every day, thereby providing rich practical examples of the theories we teach in lectures and seminars. Students are always welcome to join us in the lab either as a participant, a volunteer, during a summer job, an internship, or for a master thesis.
Feel free to contact us when you are interested in one of the projects listed below or if you are generally curious about our research and would like to know more.
ETH Zürich is using SiROP to publish and search scientific projects. For more information please visit external pagesirop.org.
TMS-based decoding of executed and imagined hand actions
Neurofeedback (NF) is a promising approach for training healthy participants and patients to modulate their motor-related neural activity even in the absence of overt motor output. Motor imagery (MI)-based training, i.e., participants mentally simulate movements, also has beneficial effects on the restoration of impaired motor function. Transcranial magnetic stimulation (TMS) is a non-invasive, low-risk method that is routinely used for psychological or neuroscientific research in human participants. In comparison to electroencephalography, TMS-based NF has great potential to distinguish fine-grained MI tasks such as different hand actions. This is important because daily life activities require complex coordination of hand muscles. A hand function training is critical for individuals with impaired hand function. Our group has developed a new protocol that uses TMS to detect MI-induced motor activity patterns in the primary motor cortex. Here we will use TMS over the primary motor cortex of participants to measure motor evoked potentials (MEPs) in finger muscles during either motor execution or motor imagery of different hand actions, namely holding a bottle, turning a key and opening the hand. Based on the MEPs we provide participants visual feedback. We aim to further develop and validate an online, adaptive classification algorithm that decodes imagined hand actions in healthy volunteers from TMS-evoked MEPs and potentially apply this to stroke survivors.
Our group has recently completed the pilot data acquisition investigating the performance of an adaptive classification algorithm for decoding imaged hand actions during TMS-based NF training. We will continue with data collection and include brain MRI scans to further develop and validate this novel TMS-based NF training protocol.
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