At the end of this course, students are able to:
- record and process neural signals (EEG, EMG)
- apply and develop machine learning algorithms on neural data
- integrate sensory feedback mechanisms
- develop assistive systems with which the pilots can complete everyday life tasks in the context of the Cybathlon competition
Additionally, non-technical skills are learned: The students are able to:
- organize and lead a large technological project
- work in small teams and communicate the team's progress
- participatorily include people with disability in their research and development work
- apply methods of human-centered design and reflect neurotechnology with respect to ethical perspectives
The students build teams to work on scientific-technological-social problems in the context of the arm prosthesis or the brain-computer interface discipline of the Cybathlon competition. In collaboration with "pilots" (persons with a physical disability, who use the technology within the Cybathlon competition) and following the human-centered design approach, the students are tasked to find socio-technical challenges and iteratively work on them. The technical focus is about recording, processing and classifying neural signals, combination with contextual information and sensory feedback. The human-centered focus is about participatory inclusion of the pilots and a value-sensitive approach.
The final goal is to participate in the Cybathlon competition and at the same time to work on scientific problems which are important for the future users of these neurotechnologies. Cybathlon is a competition in which humans with disability compete to complete everday life activities as fast as possible, while being supported by assistive technology.
The course consists of 4 phases:
- Kick-off phase: The students receive introductory lectures on neuroprostheses, brain-computer interfaces, human-centered engineering, responsible neuroengineering and disability studies. Additionally, they learn about technical methods (signal processing and machine learning for EMG- & EEG-signals).
- Project finding phase: The students develop and present project plans based on the theoretical lectures, scientific literature and the inclusion of patients' perspectives and needs.
- Iterative development phase: The students develop and implement algorithms und hardware systems. This development is connected with a testing and validation phase through objective measures and the inclusion of the user perspective. During this phase the students have regular colloquia with the lecturers to discuss their progress, challenges and next steps.
- Reflection phase: The students present their results and write a report in which they describe their technical and scientific work, but also discuss the social and ethical aspects of their work. In this report they reflect about the collaboration with the pilot, e.g. which problems arose, which solutions they found and what they learned from it.
Qualification for Cybathlon competitions:
This practical project is for students who would like to work on the challenges of the Cybathlon competition and on human-centered engineering over the period of several months.
The students should possess programming skills. Prior knowledge in machine learning and signal processing is helpful.
Teaching and learning methods
- Introductory lectures
- Application-specific tutorials: neural recording (EEG, EMG), sensory feedback, human-centered engineering
- Independent student work (including work in the laboratory with the neurotechnologies)
- group work with methods of human-centered engineering
- social events with the pilots
The examination consists of the practical part (60%), a written report (20%), and a presentation (20%).
In the practical part, the students collaborate in small teams with the pilots (disabled people who will use the technologies in the Cybathlon competition) on a socio-technological problem in the context of the arm prosthesis discipline or the brain-computer interface discipline of the Cybathlon competition. The written report will reflect the ability of the students to analyze technological, scientific and social issues with regards to arm prostheses and brain-computer interfaces and to present their own results and experiences. The presentations shows the students' ability to summarize their research and to reflect the human-centered design approach.
Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation. Mikhail A. Lebedev, Miguel A. L. Nicolelis. Physiological reviews 2017 - Brain-Computer Interfacing: An Introduction, Rajesh P.N. Rao (2013) - Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control. Inaki Iturrate, Ricardo Chavarriaga, Luis Montesano, Javier Minguez, José del R Millán (2015), Scientific Reports - The Human Use of Human Beings. Norbert Wiener (1950) - Human-Machine Symbiosis: The Foundations of Human-centred Systems Design. Editor: Karamjit S. Gill (1996)