This project investigates the relationship between cortical activity (Electroencephalography, EEG), eye movements and mental representation structures (Structural Dimensional Analysis of Mental Representation, SDA-M) as a complex measure of ongoing cognitive processes during human-machine interaction (HMI) - an approach that has so far received little attention.
Every year thousands of people in Germany suffer severe traumatic brain injuries resulting in a permanent loss of consciousness and the ability to communicate. Our project aims at combining innovative interaction concepts with novel technology to improve the every day life of patients, care takers and relatives. We develop an adaptive, multi-modal training and communication system based on neural activity (EEG) and other sensor data to reinforce the patient's residual reactions and build up basal communication possibilities.
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.
This project investigates the relationship between cortical activity (Electroencephalography, EEG), eye movements and mental representation structures (Structural Dimensional Analysis of Mental Representation, SDA-M) as a complex measure of ongoing cognitive processes during human-machine interaction (HMI) - an approach that has so far received little attention.
Every year thousands of people in Germany suffer severe traumatic brain injuries resulting in a permanent loss of consciousness and the ability to communicate. Our project aims at combining innovative interaction concepts with novel technology to improve the every day life of patients, care takers and relatives. We develop an adaptive, multi-modal training and communication system based on neural activity (EEG) and other sensor data to reinforce the patient's residual reactions and build up basal communication possibilities. 
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.