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Abstract: Invasive brain signal recordings generally rely on bioelectrodes implanted on the cortex underneath the dura. Subdural recordings have strong advantages in terms of bandwidth, spatial resolution and signal quality. However, subdural electrodes also have the drawback of compromising the long-term stability of such implants and heighten the risk of infection. Epidurally implanted electrodes might provide a viable alternative to subdural electrodes, offering a compromise between signal quality and invasiveness. Determining the feasibility of epidural electrode implantation for e.g., clinical research, brain–computer interfacing (BCI) and cognitive experiments, requires the characterization of the electrical properties of the dura, and its effect on signal feature detection. In this paper we report measurements of brain signal attenuation by the human dura in vivo. In addition, we use signal detection theory to study how the presence of the dura between the sources and the recording electrodes affects signal power features in motor BCI experiments. For noise levels typical of clinical brain signal recording equipment, we observed no detrimental effects on signal feature detection due to the dura. Subdural recordings were found to be more robust with respect to increased instrumentation noise level as compared to their epidural counterpart nonetheless. Our findings suggest that epidural electrode implantation is a viable alternative to subdural implants from the feature detection viewpoint. [Copyright &y& Elsevier]

Abstract: In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis. [Copyright &y& Elsevier]

• We tested a 3D scanner as an affordable and fast electrode digitizer. • The accuracy of the 3D scanned electrode positions is higher than template positions. • EEG source models improve with 3D scanned electrode positions. In this study, we evaluated the use of a structured-light 3D scanner for EEG electrode digitization. We tested its accuracy, robustness and evaluated its practical feasibility. Furthermore, we assessed how 3D scanning of EEG electrode positions affects the accuracy of EEG volume conduction models and source localization. To assess the improvement in electrode positions and source results, we compared the electrode positions both at the scalp level and by quantifying source model accuracy between the 3D scanner, generic template, and cap-specific electrode positions. The use of the 3D scanner significantly improves the accuracy of EEG electrode positions to a median error of 9.4 mm and maximal error of 32.8 mm, relative to the custom (median error of 10.9 mm, maximal error 39.1 mm) and manufacturer's template positions (median error of 13.8 mm, maximal error 57.0 mm). The relative difference measure (RDM) of the EEG source model averaged over the brain improves from 0.18 to 0.11. The dipole localization error averaged over the brain improves from 11.4 mm to 7.0 mm. A structured-light 3D scanner improves the electrode position accuracy and thereby the EEG source model accuracy. It is more affordable than systems currently used for this, and allows for robust and fast digitization. Therefore, we consider it a cost and time-efficient way to improve EEG source reconstruction. [ABSTRACT FROM AUTHOR]