Researchers are increasingly attempting to undertake electroencephalography (EEG) recordings in novel environments and contexts outside of the traditional static laboratory setting. The term “mobile EEG,” although commonly used to describe many of these undertakings, is ambiguous, since it attempts to encompass a wide range of EEG device mobility, participant mobility, and system specifications used across investigations. To provide quantitative parameters for “mobile EEG,” we developed a Categorisation of Mobile EEG (CoME) scheme based upon scoring of device mobility (D, from 0, off-body, to 5, head-mounted with no additional equipment), participant mobility (P, from 0, static, to 5, unconstrained running), system specification (S, from 4, lowest, to 20, highest), and number of channels (C) used. The CoME scheme was applied to twenty-nine published mobile EEG studies. Device mobility scores ranged from 0D to 4D, participant mobility scores from 0P to 4P, and system specification scores from 6S to 17S. The format of the scores for the four parameters is given, for example, as (2D, 4P, 17S, 32C) and readily enables comparisons across studies. Our CoME scheme enables researchers to quantify the degree of device mobility, participant mobility, and system specification used in their “mobile EEG” investigations in a standardised way.

Virtual reality head mounted display (VR HMD) systems are increasingly utilised in combination with electroencephalography (EEG) in the experimental study of cognitive tasks. The aim of our investigation was to determine the similarities/differences between VR HMD and the computer screen (CS) in response to an n-back working memory task by comparing visual electrophysiological event-related potential (ERP) waveforms (N1/P1/P3 components). The same protocol was undertaken for VR HMD and CS with participants wearing the same EEG headcap. ERP waveforms obtained with the VR HMD environment followed a similar time course to those acquired in CS. The P3 mean and peak amplitudes obtained in VR HMD were not significantly different to those obtained in CS. In contrast, the N1 component was significantly higher in mean and peak amplitudes for the VR HMD environment compared to CS at the frontal electrodes. Significantly higher P1 mean and peak amplitudes were found at the occipital region compared to the temporal for VR HMD. Our results show that successful acquisition of ERP components to a working memory task is achievable by combining VR HMD with EEG. In addition, the higher amplitude N1/P1 components seen in VR HMD indicates the potential utility of this VR modality in the investigation of early ERPs. In conclusion, the combination of VR HMD with EEG/ERP would be a useful approach to advance the study of cognitive function in experimental brain research.

The feasibility of a non-invasive EEG-based hypoglycaemia warning system for personal monitoring in the home has been demonstrated in a single case study. The results suggest that further studies are warranted to evaluate the system prospectively in a larger group of participants.

This paper describes an approach to the teaching of the design and development of embedded computer systems. Traditional lectures in computer architecture and real time software are complemented by project-based learning with appropriate support sessions. The logistics of the group project are described, as is the approach to assessment. Student response is described anecdotally and future plans for the project are discussed.