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About the project

Neurotechnology for 24/7 mental state monitoring

Neurotechnology advances dramatically and holds great promise in personalised medicine and patient monitoring, neuroprosthetics, in neuroeconomics and marketing, and in future brain-based augmented reality interfaces. Many of these applications are based on mental state decoding, i.e. the process of estimating the perceptual or cognitive state of the human brain from external measures. Current mental state decoding technology based on functional magnetic resonance (fMRI), magnetoencephalography (MEG) or conventional electroencephalography (EEG) do not permit decoding under naturalistic conditions due to the lack of comfort and mobility. In the Neuro 24/7 project, we apply three new high comfort and wearable devices: an implantable EEG monitor made by HypoSafe A/S, the non-invasive EarEEG platform developed at Aarhus University and the smartphone based brain scanner made by the Technical University of Denmark. All three devices are aimed at continuous long term brain state monitoring.

The vision of bringing brain monitoring into everyday life by means of wearable EEG devices relies on what we metaphorically call the keyhole hypothesis. Vast amount of prior knowledge about the function of the brain combined with information of subject specific brain processing using conventional brain monitoring techniques, such as fMRI, MEG and EEG, establishes what we metaphorically think of as observing the brain through an open door. Based on this prior knowledge and supported by other context modalities, it is hypothesised that by recording brain waves from a very limited number of EEG channels, which we metaphorically think of as observing the brain through a keyhole, it is possible to estimate current mental states of the brain.

The project will establish a research platform based on three existing EEG monitoring devices. This platform combines EEG signals and other modalities provided through or by the smartphone interface such as audio, location information and acceleration patterns, and provide the computational platform for decoding the mental states. The project will in particular research and implement computational algorithms that will allow the decoding of mental state spaces.