The Multimodal Brain-Body Systems group develops methods and technologies for the multimodal acquisition, AI-based processing and interpretation of neurophysiological, physiological and behavioural data. In doing so, we combine artificial intelligence and machine learning techniques with methods from digital signal processing, statistics and modelling. The aim is to reliably measure neural, physical and behavioural states and their changes, and to gain a better understanding of the dynamic interactions between the brain, body and behaviour.

A key methodological focus is on the robust analysis and integration of complex multimodal data. This includes signal processing, artefact detection and correction, feature extraction, data fusion and AI-based modelling. Neurophysiological measurement and imaging techniques, as well as biosignals, are linked with movement and activity data, and data from wearables and apps. On this basis, we develop digital biomarkers and digital phenotyping approaches to map individual states, trajectories and changes continuously and in context.

Particular emphasis is placed on transferring these methods into clinical and real-life application contexts, extending to use in the home environment. The focus is on applications in the fields of mental health, neurology and rehabilitation. The approaches developed range from digital monitoring and early detection, through prognosis and personalised support, to adaptive neurotechnologies. These include, amongst others, brain-computer interfaces and neurofeedback, as well as AI-supported systems to assist mental, cognitive and physical functions.