EMOIO Research Project

Adaptive assistance systems are able to support the user in a wide range of different situations. These systems take external information and attempt to deduce user intentions from the context of use, without requiring or allowing direct feedback from the user. For this reason, it remains unclear whether the system’s behavior was in accordance with the user’s intentions – leading to problems in the interaction between human and adaptive technology. The goal of the EMOIO project is to overcome potential barriers of use with the aid of neuroscientific methods. Merging ergonomics with the neurosciences into the new field of neuroergonomics research produces enormous potential for innovation, to make the symbiosis between humans and technology more intuitive. To this end, brain-computer interfaces (BCIs) offer a new generation of interfaces between humans and technology. BCIs make it possible to register mental states such as attention and emotions and transmit this information directly to a technological system. So-called neuroadaptive systems continuously use this information in order to adjust the behavior, functions or the content of an interactive system accordingly. A neuroadaptive system is being developed by a consortium of partners from research and industry as part of the EMOIO project. The goal of the system is to recognize, based on the users’ brain activity, whether system-initiated behaviors are approved or rejected. The system is able to use this information to provide the person with the best possible assistance and thus adapt to individual and situational demands. To do this, neuroscientific methods such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) are being evaluated with respect to their suitability for measuring emotions (approval/rejection).In addition, a corresponding algorithm is being developed for real-time emotional recognition. The miniaturization and resilience of the EEG and fNIRS sensors are also being promoted. Finally, the developed system is being explored in three different areas of application: web-based adaptive user interfaces, vehicle interaction, and human-robot collaboration.