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Objective. Electroencephalography (EEG) cleaning has been a longstanding issue in the research community. In recent times, huge leaps have been made in the field, resulting in very promising techniques to address the issue. The most widespread ones rely on a family of mathematical methods known as blind source separation (BSS), ideally capable of separating artefactual signals from the brain originated ones. However, corruption of EEG data still remains a problem, especially in real life scenario where a mixture of artefact components affects the signal and thus correctly choosing the correct cleaning procedure can be non trivial. Our aim is here to evaluate and score the plethora of available BSS-based cleaning methods, providing an overview of their advantages and downsides and of their best field of application. Approach. To address this, we here first characterized and modeled different types of artefact, i.e. arising from muscular or blinking activity as well as from transcranial alternate current stimulation. We then tested and scored several BSS-based cleaning procedures on semi-synthetic datasets corrupted by the previously modeled noise sources. Finally, we built a lifelike dataset affected by many artefactual components. We tested an iterative multistep approach combining different BSS steps, aimed at sequentially removing each specific artefactual component. Main results. We did not find an overall best method, as different scenarios require different approaches. We therefore provided an overview of the performance in terms of both reconstruction accuracy and computational burden of each method in different use cases. Significance. Our work provides insightful guidelines for signal cleaning procedures in the EEG related field.

Acquired brain injuries, such as stroke, are a major cause of long-term disability worldwide. When an injury occurs within primary motor cortex (M1) there is both a loss of descending information to the spinal cord and a disruption in the communication within and between spared regions, which can lead to hemiparesis and other motor dysfunctions. Recently, activity-dependent stimulation (ADS), a closed loop intracortical neurostimulation technique, was shown to significantly improve behavioural recovery in rodent models following a unilateral traumatic brain injury in the primary motor cortex. While behavioural benefits of this approach have been described, the neurophysiological changes have not yet been fully characterized. In this study, we investigated how neural activity in the form of local field potential (LFP) was impacted by an ischemic lesion and how it changed following prolonged ADS sessions. Intracortical microelectrodes arrays were used to record and stimulate from the rostral forelimb area (RFA) and the primary somatosensory cortex (S1) of 12 rats (Long Evans). An ischemic injury induced with Endothelin-1 microinjections in the caudal forelimb area (CFA) was used as an injury model. We found that the ET-1 caused lesion led to widespread drop of LFP power across all the frequency bands in both the monitored areas. The lesion did not change the functional connectivity within the brain areas, while it increased the synchronization between them. Following stimulation, LFP power generally increased in both areas. Stimulation also led to a decrement in connectivity between S1 and RFA, while causing the within S1 connectivity to increase.

As our understanding of volitional motor function increases, it is clear that complex movements are the result of the interactions of multiple cortical regions rather than just the output properties of primary motor cortex. However, our understanding of the interactions among these regions is limited. In this study, we used the activity-dependent stimulation (ADS) technique to determine the short/long-term effects on network activity and neuroplasticity of intracortical connections. ADS uses the intrinsic neural activity of one region to trigger stimulations in a separate region of the brain and can manipulate neuronal connectivity in vivo. Our aim was to compare single-unit neuronal activity within premotor cortex (rostral forelimb area, [RFA] in rats) in response to ADS (triggered from RFA) and randomly-generated stimulation in the somatosensory area (S1) within single sessions and across 21 consecutive days of stimulation. We examined firing rate and correlation between spikes and stimuli in chronically-implanted healthy ambulatory rats during spontaneous and evoked activity. At the end of the treatment, we evaluated changes of synaptophysin expression. Our results demonstrated the ability of ADS to modulate RFA firing properties and to promote synaptogenesis in S1, strengthening the idea that this Hebbian-inspired protocol can be used to modulate cortical connectivity.

Working memory (WM) performance is very often measured using the n‐back task, in which the participant is presented with a sequence of stimuli, and required to indicate whether the current stimulus matches the one presented n steps earlier. In this study, we used high‐density electroencephalography (hdEEG) coupled to source localization to obtain information on spatial distribution and temporal dynamics of neural oscillations associated with WM update, maintenance and readout. Specifically, we a priori selected regions from a large fronto‐parietal network, including also the insula and the cerebellum, and we analyzed modulation of neural oscillations by event‐related desynchronization and synchronization (ERD/ERS). During update and readout, we found larger θ ERS and smaller β ERS respect to maintenance in all the selected areas. γLOW and γHIGH bands oscillations decreased in the frontal and insular cortices of the left hemisphere. In the maintenance phase we observed decreased θ oscillations and increased β oscillations (ERS) in most of the selected posterior areas and focally increased oscillations in γLOW and γHIGH bands in the frontal and insular cortices of the left hemisphere. Finally, during WM readout, we also found a focal modulation of the γLOW band in the left fusiform cortex and cerebellum, depending on the response trial type (true positive vs. true negative). Overall, our study demonstrated specific spectral signatures associated with updating of memory information, WM maintenance, and readout, with relatively high spatial resolution., Specific spectral signatures are associated with updating of memory information, working memory maintenance, and readout in a large fronto‐parietal network, including also the insula and the cerebellum.

As our understanding of how motor output is generated increases, it is clear that there is a need to understand the interactions of multiple distinct regions rather than just the output properties of primary motor cortex. This becomes even more imperative when trying to understand how different regions may contribute to recovery following injury. In this study we used a technique that promotes functional motor recovery after injury, activity-dependent stimulation (ADS), to determine the short- and long-term effects on network activity and neuroplasticity of intracortical connections. ADS uses recorded neural activity to trigger stimulation of the brain and may be utilized to manipulate neuronal connectivity in vivo, representing a novel technique to shape intrinsic neuroplasticity. The aim of this work was to compare the effect of ADS to randomly-generated stimulation (RS) of the somatosensory area (S1) on the single units’ patterns of activity taking place in the premotor cortex (RFA) and to investigate whether synaptic plasticity changes occur in S1 as a consequence of 21 consecutive days of stimulation. In particular, we examined both firing rate changes and correlation between spiking activity and stimuli in chronically-implanted healthy ambulatory rats during both spontaneous and evoked activity, resulting from the two stimulation paradigms. Finally, we evaluated changes in expression of synaptophysin at the end of the treatment. This experimental procedure demonstrated the ability of ADS to modulate firing properties of RFA within daily recording sessions and to promote synaptogenesis in S1, further strengthening the idea that this Hebbian-inspired protocol can be used to effectively modulate cortical connectivity and thus suggesting its translational potential for promoting recovery after brain injury.

Working memory (WM) performance is very often measured using the n-back task, in which the participant is presented with a sequence of stimuli, and required to indicate whether the current stimulus matches the one presented n steps earlier. In this study, we used high-density electroencephalography (hdEEG) coupled to source localization to obtain information on spatial distribution and temporal dynamics of neural oscillations associated with WM update, maintenance and readout. Specifically, we a priori selected regions from a large fronto-parietal network, including also the insula and the cerebellum, and we analyzed modulation of neural oscillations by event-related desynchronization and synchronization (ERD/ERS).During update and readout, we found larger θ ERS and smaller β ERS respect to maintenance in all the selected areas. γLOWand γHIGHbands oscillations decreased in the frontal and insular cortices of the left hemisphere. In the maintenance phase we observed focally decreased θ oscillations and increased β oscillations (ERS) in most of the selected posterior areas and focally increased oscillations in γLOWand γHIGHbands in the frontal and insular cortices of the left hemisphere. Finally, during WM readout, we also found a focal modulation of the γLOWband in the left fusiform cortex and cerebellum, depending on the response trial type (true positive vs. true negative).Overall, our study demonstrated specific spectral signatures associated with updating of memory information, WM maintenance and readout, with relatively high spatial resolution.

Goal: Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrates a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks. ispartof: IEEE Open Journal of Engineering in Medicine and Biology vol:1 pages:57-64 ispartof: location:United States status: published

Recent advances in the field of human brain imaging by electrophysiological recordings allow to reliably quantify the connectivity patterns of spontaneous oscillatory activity. This has provided novel tools to investigate the neural basis underlying complex human behavior and to unravel the mechanisms of brain function and reorganization in response to neurological diseases. In this context, we present a pipeline integrating high-density electroencephalography (hd-EEG) analysis and graph theory. To test our pipeline, we conducted a feasibility study on hd-EEG analysis to recordings from healthy individuals, examining the frequency-specific small-world organization of brain connectivity. Here we present the preliminary results on a small sample size. The ultimate goal of our study is to extract graph-theory related metrics, such as small-worldness index, from injured patients and use them as electrophysiological biomarkers of the recovery.

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. ispartof: Journal of Neural Engineering vol:15 issue:5 ispartof: location:England status: published