Development of mobile sensors brings new opportunities to medical research. In particular, mobile electroencephalography (EEG) devices can be potentially used in low cost screening for epilepsy and other neurological and psychiatric disorders. The necessary condition for such applications is thoughtful validation in the specific medical context. As part of validation and quality assurance, we develop a computer-based analysis pipeline, which aims to compare the EEG signal acquired by a mobile EEG device to the one collected by a medically approved clinical-grade EEG device. Both signals are recorded simultaneously during 30 minutes long sessions in resting state. The data are collected from 22 patients with epileptiform abnormalities in EEG. In order to compare two multichannel EEG signals with differently placed references and electrodes, a novel data processing pipeline is proposed. It allows deriving matching pairs of time series which are suitable for similarity assessment through Pearson correlation. The average correlation of 0.64 is achieved on a test dataset, which can be considered a promising result, taking the positions shift due to the simultaneous electrode placement into account.

With the introduction of autonomous vehicles, drivers will be able to engage in non-related tasks while being driven. But in critical situations the car needs the support of the human driver. How do distracted drivers get back into the control-loop quickly when the car requests a take-over? To investigate effective take-over actions, we developed an interactive virtual reality experiment, that uses premises of the embodied cognition theory. Accordingly, the car should not only provide sensory input, but also help enhance the driver’s motor response by interpreting intention and thus helping to accomplish desired actions. This binds humans and machines together in becoming true cooperation partners in joint action. Therefore, we aim for a close monitoring of participants combined with sensorimotor feedforward and feedback. The presented prototype also serves as an open-access, cost-efficient toolkit that enables interested researchers to tailor the presented LoopAR tool to their own needs as part of a previously published toolkit called WestDrive. With the presented work, we hope to shift the paradigm of future research from only visual aids to full sensorimotor integration assistance.

Development of mobile sensors brings new opportunities to medical research. In particular, mobile electroencephalography (EEG) devices can be potentially used in low cost screening for epilepsy and other neurological and psychiatric disorders. The necessary condition for such applications is thoughtful validation in the specific medical context. As part of validation and quality assurance, we develop a computer-based analysis pipeline, which aims to compare the EEG signal acquired by a mobile EEG device to the one collected by a medically approved clinical-grade EEG device. Both signals are recorded simultaneously during 30 minutes long sessions in resting state. The data are collected from 22 patients with epileptiform abnormalities in EEG. In order to compare two multichannel EEG signals with differently placed references and electrodes, a novel data processing pipeline is proposed. It allows deriving matching pairs of time series which are suitable for similarity assessment through Pearson correlation. The average correlation of 0.64 is achieved on a test dataset, which can be considered a promising result, taking the positions shift due to the simultaneous electrode placement into account.