Finding an optimal learning strategy not only for machines but also for humans is central to a large research effort dedicated to development of ITS. Creating a computational environment that enables human learners to gain full control of their learning efficiency is
Learning of a new skill is a process that is commonly accompanied by an expert. Inspired by the MAML [1] the goal of this thesis is to implement a meta-learner to accelerate human learning. We will develop a tool that teaches a novice to play piano, guided by the dexmo exoskeleton.
Acquiring a profound knowledge about the cognitive processes underlying human-robot interaction is essential to better exploit the measurable components for brain-robot interfaces. The better the processes are understood, the better the EEG components originating from these processes can be used. A systematic evaluation of these components in connection with human-robot interaction is missing until today. Hence, it appears to be worthwhile to take a closer and impartial look at what is really happening on the cognitive level, as far as determinable by EEG signals.
MONARCA will develop and validate solutions for multi-parametric, long term monitoring of behavioural and physiological information relevant to bipolar disorder.
We explore a new BCI design for the control of robotic devices. Specifically, we show the first use of a code-modulating, Visually-Evoked Potential (cVEP)-based BCI for a navigation and control task.
Finding an optimal learning strategy not only for machines but also
Learning of a new skill is a process that is commonly accompanied by an expert. Inspired by the MAML [1] the goal of this thesis is to implement a meta-learner to accelerate human learning. We will develop a tool that teaches a novice to play piano, guided by the dexmo exoskeleton.
Acquiring a profound knowledge about the cognitive processes underlying human-robot interaction is essential to better exploit the measurable components for brain-robot interfaces. The better the processes are understood, the better the EEG components originating from these processes can be used. A systematic evaluation of these components in connection with human-robot interaction is missing until today. Hence, it appears to be worthwhile to take a closer and impartial look at what is really happening on the cognitive level, as far as determinable by EEG signals.
MONARCA will develop and validate solutions for multi-parametric, long term monitoring of behavioural and physiological information relevant to bipolar disorder.
We explore a new BCI design for the control of robotic devices. Specifically, we show the first use of a code-modulating, Visually-Evoked Potential (cVEP)-based BCI for a navigation and control task.