Neurotechnology holds great societal promise in personalized medicine and patient monitoring, in neuroprosthetics such as hearing aids, in neuroeconomics and marketing, and in future brain-based augmented reality interfaces. These applications will be based on permanent or long term ‘mental state decoding’, i.e., the process of estimating the perceptual or cognitive state of the human brain from quantitative measures. The main barriers to wide spread application of long term mental state decoding is lack of mobility, comfort, and robust decoding schemes in current neurotechnology.

In this project we will make the first steps towards enriching three promising EEG devices with smartphone sensing and advanced machine learning to enable detailed, yet robust 24/7 decoding of mental states embedded within everyday life. For long term monitoring, we will use two high comfort systems both developed in Denmark, signifying the unique national strategic position: 1) ‘ear-EEG’ by partner Preben Kidmose et al. at Aarhus University, 2) the subcutaneous EEG device produced by partner HypoSafe A/S. To define and characterize individual mental states we will use and further expand a third Danish system: The only fully mobile, real-time EEG imaging solution, the ‘Smartphone Brain Scanner‘ (SBS) developed at DTU by main applicant Lars Kai Hansen et al. Importantly, to enrich mental state definitions we make initial steps towards an advanced mobile technology that samples personal state variables such as location, acceleration patterns, and social interaction. Modeling, integration, and validation will be based on signal processing and statistical machine learning methods.