Bachelor and Master theses

In the following list are topics for both Master or Bachelor theses, please get in touch with the contact person listed, to find out more (last update May 2020).

Master Theses

Topic: ErrP-based assessment of human-agent collaboration in shared workspace and shared responsibility scenarios (already taken)

Advisor: Stefan Ehrlich

Previous works have proposed that error-related potentials (ErrPs) can be used to improve human-machine interaction (HMI)1. Ehrlich and Cheng, 20182 have shown that ErrPs can be used as a feedback to adapt the behavior of agents to the human needs in a co-adaptive scenario. Furthermore, Wirth et al., 20193 have shown that event-related potentials (ERPs) from two similar stimuli that evoke errors have different characteristics.

This thesis aims to investigate the different characteristics of the ErrPs that result from errors evoked by mistakes done from the agent and from the computer interface under two different scenarios. The goal is to better understand the ErrPs, to evaluate the possibility of understanding the stimuli of the error that can be used for future development of brain-computer interfaces for HMI.

References:

  • Chavarriaga, R., Sobolewski, A., & Millán, J. D. R. (2014). Errare machinale est: the use of error-related potentials in brain-machine interfaces. Frontiers in neuroscience, 8, 208.
  • Ehrlich, S. K., & Cheng, G. (2018). Human-agent co-adaptation using error-related potentials, Journal of Neural Engineering, 15(6), 066014.
  • Wirth, C., Dockree, P. M., Harty, S., Lacey, E. & Arcaneh, M. (2019). Towards error categorisation in BCI: single-trial EEG classification between different errors, Journal of Neural Engineering, 17(1), 016008.

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Topic:Evolution of functional connectivity across training in motor-imagery BCI tasks (already taken)

Advisor: Stefan Ehrlich

Abstract:

Motor imagery (MI) consists in imagining performing a movement without actual activation of muscles. It is widely used in EEG-based brain-computer interface (BCI) paradigms. MI tasks are however not trivial and require training subjects adequately. MI tasks are also known to enhance performance in motor execution, when done in parallel to physical training [3], suggesting that MI and motor execution are using overlapping learning processes. It has been shown that functional connectivity (FC) was impacted by MI-BCI tasks [2], and even further, that it indicates an underlying learning process as the ability to control improved, and it could predict the learning rate in the subsequent trial [1] However, it remains unknown how the network evolves over training, or what training paradigms are more efficient to modify the FC. In particular, identifying consistent components across subjects of the functional network when a threshold of ability to control is reached would help understand neural mechanisms that guarantee the function of motor imagery. This achievement would mean the generalizability of MI paradigms for rehabilitation, athletic training and limb replacement. The goal of this project is to study the evolution of the functional connectome throughout training of participants in order to identify learning processes linked to MI, connectivity patterns linked to proficiency in the BCI task and inter-subject and inter-paradigm differences.

References:

[1] Corsi, Marie-Constance; Chavez, Mario; Schwartz, Denis; George, Nathalie; Hugueville, Laurent; Kahn, Ari E. et al. (2020) Functional disconnection of associative cortical areas predicts performance during BCI training. In : NeuroImage, vol. 209, p. 116500. DOI: 10.1016/j.neuroimage.2019.116500.

[2] Mottaz, Anaïs; Corbet, Tiffany; Doganci, Naz; Magnin, Cécile; Nicolo, Pierre; Schnider, Armin; Guggisberg, Adrian G. (2018) Modulating functional connectivity after stroke with neurofeedback: Effect on motor deficits in a controlled cross-over study. In : NeuroImage. Clinical, vol. 20, p. 336–346. DOI: 10.1016/j.nicl.2018.07.029.

[3] Schuster, Corina; Hilfiker, Roger; Amft, Oliver; Scheidhauer, Anne; Andrews, Brian; Butler, Jenny et al. (2011) Best practice for motor imagery: a systematic literature review on motor imagery training elements in five different disciplines. In : BMC medicine, vol. 9, p. 75. DOI: 10.1186/1741-7015-9-75.

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Topic: Multi-limb low-impact locomotion for humanoid robots (already taken)

Supervisor: Julio Rogelio Guadarrama Olvera

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Topic: Whole body control for competitive mobile service robots (already taken)

Supervisor: Julio Rogelio Guadarrama Olvera


Bachelor Theses

Topic: Encoding different temperature levels using the ICS Robot Skin (already taken)

Advisor: Zied Tayeb / Dr. Emmanuel Carlos Dean Leon
Skin is a very important sensor for human beings. Up to 5 million discrete receptors of different modalities (e.g. temperature, force, and vibration) are distributed close to our bodies ' surface. The skin helps us to learn more about our environment and how we can safely interact with it. The aim of this project is to distinguish and encode different measured temperature ranges using the ICS Robot skin. These encoded levels can be translated thereafter into the stimulation of an amputee's arm and/or can be used as in the context of real-time human-robot interaction.

References:

  • Cheng, Gordon; Dean-Leon, Emmanuel; Bergner, Florian; Olvera, Julio Rogelio Guadarrama; Leboutet, Quentin; Mittendorfer, Philipp: A Comprehensive Realization of Robot Skin: Sensors, Sensing, Control, and Applications. Proceedings of the IEEE Volume 107 (10), 2019.
  • Zied Tayeb, Nicolai Waniek, Juri Fedjaev, Nejla Ghaboosi, Leonard Rychly, Christian Widderich, Christoph Richter, Jonas Braun, Matteo Saveriano, Gordon Cheng, Jörg Conradt, 'Gumpy: A Python toolbox suitable for hybrid brain-computer interfaces', Journal of neural engineering, Volume 15 (6), 2018.