At the Cognitive Systems Lab, we design innovative, adaptive interfaces between humans and machines. A use case for us, systems that detect biological signals by means of a user state and to respond appropriately. The internship focuses on the recording and analysis of biological signals (eg heart rate, skin conductance, respiration) to capture emotional and cognitive processes of humans. After an introduction to design the sensors used, the students under the guidance of an experiment to record relevant data. These will be first preprocessed and then used for feature extraction. These features are then analyzed using statistical tools, interpreted and used to improve the human-machine interaction.
This semester will examine whether it can be detected by bio-signals, when a man-machine interface behaves incorrectly. Specifically, two systems to be built and connected: The user of a gesture-based user interface is wearing a glove with acceleration and rotation sensors. Their data are automatically analyzed to detect different gestures may be, their impact will be displayed on a screen. While the use of EEG is recorded by the brain activity of the user. The EEG signal can identify distinctive patterns that are typical for error situation. We want to recognize these patterns and thus automatically detect whether the gesture of the user interface has been correctly recognized. If an error occurs, the interface can then offer an alternative results or repair facilities.
The internship is an extensive range of hardware and software tools already available, so that students acquire knowledge through a variety of different tools and techniques.