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. FINDINGS: Here we present the Noninvasive Electrophysiology Toolbox (NET), an open-source software for large-scale analysis of hdEEG data, running on the cross-platform MATLAB environment. NET combines all the tools required for a complete hdEEG analysis workflow, from raw signals to final measured values. By relying on reconstructed neural signals in the brain, NET can perform traditional analyses of time-locked neural responses, as well as more advanced functional connectivity and brain mapping analyses. The extracted quantitative neural data can be exported to provide broad compatibility with other software. CONCLUSIONS: NET is freely available (https://github.com/bind-group-kul/net) under the GNU public license for non-commercial use and open-source development, together with a graphical user interface (GUI) and a user tutorial. While NET can be used interactively with the GUI, it is primarily aimed at unsupervised automation to process large hdEEG datasets efficiently. Its implementation creates indeed a highly customizable program suitable for analysis automation and tight integration into existing workflows.

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

Objective.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. ocular, myogenic) and non-biological (e.g. movement-related) artifacts, which-depending on their extent-may limit the interpretability of the study results. Blind source separation (BSS) approaches have demonstrated to be particularly promising for the attenuation of artifacts in high-density EEG (hdEEG) data. Previous EEG artifact removal studies suggested that it may not be optimal to use the same BSS method for different kinds of artifacts.Approach.In this study, we developed a novel multi-step BSS approach to optimize the attenuation of ocular, movement-related and myogenic artifacts from hdEEG data. For validation purposes, we used hdEEG data collected in a group of healthy participants in standing, slow-walking and fast-walking conditions. During part of the experiment, a series of tone bursts were used to evoke auditory responses. We quantified event-related potentials (ERPs) using hdEEG signals collected during an auditory stimulation, as well as the event-related desynchronization (ERD) by contrasting hdEEG signals collected in walking and standing conditions, without auditory stimulation. We compared the results obtained in terms of auditory ERP and motor-related ERD using the proposed multi-step BSS approach, with respect to two classically used single-step BSS approaches.Main results. The use of our approach yielded the lowest residual noise in the hdEEG data, and permitted to retrieve stronger and more reliable modulations of neural activity than alternative solutions. Overall, our study confirmed that the performance of BSS-based artifact removal can be improved by using specific BSS methods and parameters for different kinds of artifacts.Significance.Our technological solution supports a wider use of hdEEG-based source imaging in movement and rehabilitation studies, and contributes to the further development of mobile brain/body imaging applications.

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. RT-NET relies on the Lab Streaming Layer for acquiring raw data from a large number of EEG amplifiers and for streaming the processed data to external applications. RT-NET estimates a spatial filter for artifact removal and source activity reconstruction using a calibration dataset. This spatial filter is then applied to the hdEEG data as they are acquired, thereby ensuring low latencies and computation times. Overall, our analyses show that RT-NET can estimate real-time neural activity with performance comparable to offline analysis methods. It may therefore enable the development of novel brain-computer interface applications such as source-based neurofeedback.

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. APPROACH: Our alternating current regression (AC-REG) method is an adaptive (time-varying) spatial filtering method. It relies on a data buffer of preset size, on which principal component analysis (PCA) is applied. The resulting components are used to build a spatial filter capable of regressing periodic signals in phase with the stimulation. PCA is performed each time that a new sample enters the buffer, such that the spatial filter can be continuously updated and applied to the EEG data. MAIN RESULTS: The AC-REG accuracy in terms of tACS artifact attenuation was assessed using simulated and real EEG data. Alternative offline processing techniques, such as the superimposition of moving averages (SMA) and the Helfrich method (HeM), were used as benchmark. Using simulations, we found that AC-REG can yield a more reliable reconstruction of the stimulation signal for any frequency between 1 and 80 Hz. Analysis of real EEG data of 18 healthy volunteers showed that AC-REG was able to better recover hidden neural activity as compared to SMA and HeM. Also, significantly higher correlations between power spectrum densities in tACS on and off conditions, respectively, were obtained using AC-REG (r = 0.90) than using SMA (r = 0.80) and HeM (r = 0.86). SIGNIFICANCE: Thanks to its low computational complexity, the AC-REG method can be employed in noninvasive closed-loop neuromodulation experiments, with potential applications both in healthy individuals and in neurological patients.

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. Here, we describe an extension of this method, the spatial positioning toolbox for head markers using 3D scans (SPOT3D), which integrates a graphical user interface (GUI). This enables the correction of imprecisions in EEG sensor positioning and the inclusion of additional head markers. The toolbox was validated using 3D scan data collected in four participants wearing a 256-channel hdEEG cap. We quantified the misalignment between the 3D scan and the head shape, and errors in EEG sensor locations. We assessed these parameters after using the automated approach and after manually adjusting its results by means of the GUI. The GUI overcomes the main limitations of the automated method, yielding enhanced precision and reliability of head marker positioning.

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. Online artifact removal is implemented by dynamically adjusting the spatial filter in the actual experiment, based on a linear regression technique. MAIN RESULTS: Our results showed that the BSS-REG method is capable of attenuating different kinds of artifacts, including ocular and muscular, while preserving true neural activity. Thanks to its low computational requirements, BSS-REG can be applied to low-density as well as high-density EEG data. SIGNIFICANCE: We argue that BSS-REG may enable the development of novel BCI applications requiring high-density recordings, such as source-based neurofeedback and closed-loop neuromodulation.