Large-scale analysis of neural activity and connectivity from high-density electroencephalographic data.

Introduction: High-density electroencephalography (hdEEG) is a technique used for the characterization of the neural activity and connectivity in the human brain. The analysis of EEG data involves several steps, including signal pre-processing, head modelling, source localization and activity/connectivity quantification. Visual check of the analysis steps is often necessary, making the process time- and resource-consuming and, therefore, not feasible for large datasets.

A multi-step blind source separation approach for the attenuation of artifacts in mobile high-density electroencephalography data.

Electroencephalography (EEG) is a widely used technique to address research questions about brain functioning, from controlled laboratorial conditions to naturalistic environments. However, EEG data are affected by biological (e.g.

RT-NET: real-time reconstruction of neural activity using high-density electroencephalography.

High-density electroencephalography (hdEEG) has been successfully used for large-scale investigations of neural activity in the healthy and diseased human brain. Because of their high computational demand, analyses of source-projected hdEEG data are typically performed offline. Here, we present a real-time noninvasive electrophysiology toolbox, RT-NET, which has been specifically developed for online reconstruction of neural activity using hdEEG.

A computationally efficient method for the attenuation of alternating current stimulation artifacts in electroencephalographic recordings.

Objective: Recent studies suggest that the use of noninvasive closed-loop neuromodulation combining electroencephalography (EEG) and transcranial alternating current stimulation (tACS) may be a promising avenue for the treatment of neurological disorders. However, the attenuation of tACS artifacts in EEG data is particularly challenging, and computationally efficient methods are needed to enable closed-loop neuromodulation experiments. Here we introduce an original method to address this methodological issue.

SPOT3D: Spatial positioning toolbox for head markers using 3D scans.

Recent studies have highlighted the importance of an accurate individual head model for reliably using high-density electroencephalography (hdEEG) as a brain imaging technique. Correct identification of sensor positions is fundamental for accurately estimating neural activity from hdEEG recordings. We previously introduced a method of automated localization and labelling of hdEEG sensors using an infrared colour-enhanced 3D scanner.

Online EEG artifact removal for BCI applications by adaptive spatial filtering.

Objective: The performance of brain-computer interfaces (BCIs) based on electroencephalography (EEG) data strongly depends on the effective attenuation of artifacts that are mixed in the recordings. To address this problem, we have developed a novel online EEG artifact removal method for BCI applications, which combines blind source separation (BSS) and regression (REG) analysis.

Approach: The BSS-REG method relies on the availability of a calibration dataset of limited duration for the initialization of a spatial filter using BSS.