Tracking of Mental Workload with a Mobile EEG Sensor.

The aim of the present investigation was to assess if a mobile electroencephalography (EEG) setup can be used to track mental workload, which is an important aspect of learning performance and motivation and may thus represent a valuable source of information in the evaluation of cognitive training approaches. Twenty five healthy subjects performed a three-level N-back test using a fully mobile setup including tablet-based presentation of the task and EEG data collection with a self-mounted mobile EEG device at two assessment time points. A two-fold analysis approach was chosen including a standard analysis of variance and an artificial neural network to distinguish the levels of cognitive load. Our findings indicate that the setup is feasible for detecting changes in cognitive load, as reflected by alterations across lobes in different frequency bands. In particular, we observed a decrease of occipital alpha and an increase in frontal, parietal and occipital theta with increasing cognitive load. The most distinct levels of cognitive load could be discriminated by the integrated machine learning models with an accuracy of 86%.

[Geriatric patient with neurological symptoms and prolonged aPTT]. / Geriatrischer Patient mit neurologischer Symptomatik und verlängerter aPTT.

A 73-year-old man with dementia was referred to our clinic with hypernatremia and volume depletion. New-onset neurogenic dysphagia was likely the reason for both. The patient had chronic embolic strokes on the computed tomography (CT) images. Documentation from previous hospitalizations in different hospitals revealed a repeatedly prolonged activated partial thromboplastin time (aPTT); 5 years prior, antiphospholipid antibody syndrome had already been suspected, but the necessary workup was never completed. We diagnosed the patient with primary antiphospholipid antibody syndrome and initiated therapy with vitamin K antagonists (phenprocoumon) and aspirin.