EEG System Research Articles

Letter: Using Continuous Intracranial Electroencephalography Monitoring to Manage Epilepsy Patients During COVID-19.

Letter: Using Continuous Intracranial Electroencephalography Monitoring to Manage Epilepsy Patients During COVID-19.

Analyzing and Decoding Natural Reach-and-Grasp Actions Using Gel, Water and Dry EEG Systems.

Reaching and grasping is an essential part of everybody’s life, it allows meaningful interaction with the environment and is key to independent lifestyle. Recent electroencephalogram (EEG)-based studies have already shown that neural correlates of natural reach-and-grasp actions can be identified in the EEG. However, it is still in question whether these results obtained in a laboratory environment can make the transition to mobile applicable EEG systems for home use. In the current study, we investigated whether EEG-based correlates of natural reach-and-grasp actions can be successfully identified and decoded using mobile EEG systems, namely the water-based EEG-VersatileTM system and the dry-electrodes EEG-HeroTM headset. In addition, we also analyzed gel-based recordings obtained in a laboratory environment (g.USBamp/g.Ladybird, gold standard), which followed the same experimental parameters. For each recording system, 15 study participants performed 80 self-initiated reach-and-grasp actions toward a glass (palmar grasp) and a spoon (lateral grasp). Our results confirmed that EEG-based correlates of reach-and-grasp actions can be successfully identified using these mobile systems. In a single-trial multiclass-based decoding approach, which incorporated both movement conditions and rest, we could show that the low frequency time domain (LFTD) correlates were also decodable. Grand average peak accuracy calculated on unseen test data yielded for the water-based electrode system 62.3% (9.2% STD), whereas for the dry-electrodes headset reached 56.4% (8% STD). For the gel-based electrode system 61.3% (8.6% STD) could be achieved. To foster and promote further investigations in the field of EEG-based movement decoding, as well as to allow the interested community to make their own conclusions, we provide all datasets publicly available in the BNCI Horizon 2020 database (http://bnci-horizon-2020.eu/database/data-sets).

A validation of Emotiv EPOC Flex saline for EEG and ERP research.

BackgroundPrevious work has validated consumer-grade electroencephalography (EEG) systems for use in research. Systems in this class are cost-effective and easy to set up and can facilitate neuroscience outside of the laboratory. The aim of the current study was to determine if a new consumer-grade system, the Emotiv EPOC Saline Flex, was capable of capturing research-quality data.MethodThe Emotiv system was used simultaneously with a research-grade EEG system, Neuroscan Synamps2, to collect EEG data across 16 channels during five well-established paradigms: (1) a mismatch negativity (MMN) paradigm that involved a passive listening task in which rare deviant (1,500 Hz) tones were interspersed amongst frequent standard tones (1,000 Hz), with instructions to ignore the tones while watching a silent movie; (2) a P300 paradigm that involved an active listening task in which participants were asked to count rare deviant tones presented amongst frequent standard tones; (3) an N170 paradigm in which participants were shown images of faces and watches and asked to indicate whether the images were upright or inverted; (4) a steady-state visual evoked potential (SSVEP) paradigm in which participants passively viewed a flickering screen (15 Hz) for 2 min; and (5) a resting state paradigm in which participants sat quietly with their eyes open and then closed for 3 min each.ResultsThe MMN components and P300 peaks were equivalent between the two systems (BF10 = 0.25 and BF10 = 0.26, respectively), with high intraclass correlations (ICCs) between the ERP waveforms (>0.81). Although the N170 peak values recorded by the two systems were different (BF10 = 35.88), ICCs demonstrated that the N170 ERP waveforms were strongly correlated over the right hemisphere (P8; 0.87–0.97), and moderately-to-strongly correlated over the left hemisphere (P7; 0.52–0.84). For the SSVEP, the signal-to-noise ratio (SNR) was larger for Neuroscan than Emotiv EPOC Flex (19.94 vs. 8.98, BF10 = 51,764), but SNR z-scores indicated a significant brain response at the stimulus frequency for both Neuroscan (z = 12.47) and Flex (z = 11.22). In the resting state task, both systems measured similar alpha power (BF10 = 0.28) and higher alpha power when the eyes were closed than open (BF10 = 32.27).ConclusionsThe saline version of the Emotiv EPOC Flex captures data similar to that of a research-grade EEG system. It can be used to measure reliable auditory and visual research-quality ERPs. In addition, it can index SSVEP signatures and is sensitive to changes in alpha oscillations.

Measuring Gait-Event-Related Brain Potentials (gERPs) during Instructed and Spontaneous Treadmill Walking: Technical Solutions and Automated Classification through Artificial Neural Networks

The investigation of the neural correlates of human gait, as measured by means of non-invasive electroencephalography (EEG), is of central importance for the understanding of human gait and for novel developments in gait rehabilitation. Particularly, gait-event-related brain potentials (gERPs) may provide information about the functional role of cortical brain regions in human gait control. The purpose of this paper is to explore possible experimental and technical solutions for time-sensitive analysis of human gait ERPs during spontaneous and instructed treadmill walking. A solution (hardware/software) for synchronous recording of gait and EEG data was developed, tested and piloted. The solution consists of a custom-made USB synchronization interface, a time-synchronization module, and a data-merging module, allowing the temporal synchronization of recording devices, time-sensitive extraction of gait markers for the analysis of gERPs, and the training of artificial neural networks. In the present manuscript, the hardware and software components were tested with the following devices: A treadmill with an integrated pressure plate for gait analysis (zebris FDM-T) and an Acticap non-wireless 32-channel EEG system (Brain Products GmbH). The usability and validity of the developed solution was investigated in a pilot study (n = 3 healthy participants, n = 3 females, mean age = 22.75 years). The recorded continuous EEG data were segmented into epochs according to the detected gait markers for the analysis of gERPs. Finally, the EEG epochs were used to train a deep learning artificial neural network as classifier of gait phases. The results obtained in this pilot study, although preliminary, support the feasibility of the solution for the application of gait-related EEG analysis.

Impedance and Noise of Passive and Active Dry EEG Electrodes: A Review

Dry electrodes are a promising solution for prolonged EEG signal acquisition, whereas wet electrodes may lose their signal quality in the same situation and require skin preparation for set-up. Here, we review the impedance and noise of passive and active dry EEG electrodes. In addition, we compare noise and input impedance of the EEG amplifiers. As there are multiple definitions of impedance in each EEG system, they are all first defined. Electrodes must be compatible with amplifiers to accurately record EEG signals. This implies that their impedance plays a significant role in amplifier compatibility and affects total input-referred noise. Therefore, we review the impedance and noise of state-of-the-art amplifiers and electrodes. Furthermore, we compare the various structures and materials used and their final impedance to that of wet electrodes. Finally, we compare state-of-the-art electrodes and amplifiers to the standards of the IFCN and IEC80601-2-26. We investigate bottlenecks and propose a guideline for future work on passive and active dry electrodes, as well as EEG amplifiers.

Detecting Seizures and Epileptiform Abnormalities in Acute Brain Injury.

Acute brain injury (ABI) is a broad category of pathologies, including traumatic brain injury, and is commonly complicated by seizures. Electroencephalogram (EEG) studies are used to detect seizures or other epileptiform patterns. This review seeks to clarify EEG findings relevant to ABI, explore practical barriers limiting EEG implementation, discuss strategies to leverage EEG monitoring in various clinical settings, and suggest an approach to utilize EEG for triage. Current literature suggests there is an increased morbidity and mortality risk associated with seizures or patterns on the ictal-interictal continuum (IIC) due to ABI. Further, increased use of EEG is associated with better clinical outcomes. However, there are many logistical barriers to successful EEG implementation that prohibit its ubiquitous use. Solutions to these limitations include the use of rapid EEG systems, non-expert EEG analysis, machine learning algorithms, and the incorporation of EEG data into prognostic models.

Toward an Individualized Neural Assessment of Receptive Language in Children.

Purpose We aimed to develop a noninvasive neural test of language comprehension to use with nonspeaking children for whom standard behavioral testing is unreliable (e.g., minimally verbal autism). Our aims were threefold. First, we sought to establish the sensitivity of two auditory paradigms to elicit neural responses in individual neurotypical children. Second, we aimed to validate the use of a portable and accessible electroencephalography (EEG) system, by comparing its recordings to those of a research-grade system. Third, in light of substantial interindividual variability in individuals' neural responses, we assessed whether multivariate decoding methods could improve sensitivity. Method We tested the sensitivity of two child-friendly covert N400 paradigms. Thirty-one typically developing children listened to identical spoken words that were either strongly predicted by the preceding context or violated lexical-semantic expectations. Context was given by a cue word (Experiment 1) or sentence frame (Experiment 2), and participants either made an overall judgment on word relatedness or counted lexical-semantic violations. We measured EEG concurrently from a research-grade system, Neuroscan's SynAmps2, and an adapted gaming system, Emotiv's EPOC+. Results We found substantial interindividual variability in the timing and topology of N400-like effects. For both paradigms and EEG systems, traditional N400 effects at the expected sensors and time points were statistically significant in around 50% of individuals. Using multivariate analyses, detection rate increased to 88% of individuals for the research-grade system in the sentences paradigm, illustrating the robustness of this method in the face of interindividual variations in topography. Conclusions There was large interindividual variability in neural responses, suggesting interindividual variation in either the cognitive response to lexical-semantic violations and/or the neural substrate of that response. Around half of our neurotypical participants showed the expected N400 effect at the expected location and time points. A low-cost, accessible EEG system provided comparable data for univariate analysis but was not well suited to multivariate decoding. However, multivariate analyses with a research-grade EEG system increased our detection rate to 88% of individuals. This approach provides a strong foundation to establish a neural index of language comprehension in children with limited communication. Supplemental Material https://doi.org/10.23641/asha.12606311.

Test-Retest Reliability of Time-Domain EEG Features to Assess Cognitive Load Using a Wireless Dry-Electrode System.

Human Machine Interfaces (HMIs) can provide critical support and improve daily task functionality for prosthesis users or social interaction for patients with locked-in syndrome using an assistive communication device. One goal in the development of sophisticated HMIs is to reduce the cognitive load (CL) they place on the user to promote the use of the technology. Electroencephalogram (EEG)-derived measures collected with wired wet-electrode systems have been used to assess CL in laboratory environments and have demonstrated acceptable test-retest reliability. Assessment of CL during real-world unconstrained HMI operation, however, requires the use of a wireless dry-electrode EEG system which provides easier electrode application and untethered movement. However, the test-retest reliability of wireless dry-electrode systems to quantify CL has not been explored. Ensuring the consistent capture of CL-related signals across multiple sessions is critical if these devices are to be used to assess how improvements in HMIs affect CL. Therefore, the current study used a wireless dry-electrode EEG system to compare Evoked Response Potential (ERP) features of a simple auditory oddball task to measure CL during two separate testing sessions a week apart. ERPs of 11 subjects were recorded while participants performed a virtual task at two difficulty levels. A significant correlation was found between the P300 component of the ERPs and subjective ratings of CL during both testing sessions. Furthermore, there was a statistically significant test-retest reliability for this same ERP feature and similar signal-to-noise ratios (SNRs) across sessions.Clinical Relevance- This is an initial step in validating wireless dry-electrode EEG systems to assess cognitive load across multiple sessions. The evidence presented is critical if dry-wireless EEG systems are to be used to identify aspects of HMIs that reduce CL in clinical and real-life environments. Assessing CL in unconstrained environments can better inform clinicians and technology developers in their design of future HMIs.

Novel Electrodes for Reliable EEG Recordings on Coarse and Curly Hair.

EEG is a powerful and affordable brain sensing and imaging tool used extensively for the diagnosis of neurological disorders (e.g. epilepsy), brain computer interfacing, and basic neuroscience. Unfortunately, most EEG electrodes and systems are not designed to accommodate coarse and curly hair common in individuals of African descent. In neuroscience studies, this can lead to poor quality data that might be discarded in scientific studies after recording from a broader population set. In clinical diagnoses, it may lead to an uncomfortable and/or emotionally taxing experience, and, in the worst cases, misdiagnosis. Our prior work demonstrated that braiding hair in cornrows to expose the scalp at target locations leads to reduced electrode-skin impedance for existing electrodes. In this work, we design and implement novel electrodes that harness braided hair, and demonstrate that, across time, our electrodes, in conjunction with braiding, lower the impedance further, attaining 10x lower impedance than existing systems.

Validating a LEGO-Like EEG Headset for a Simultaneous Recording of Wet- and Dry-Electrode Systems During Treadmill Walking.

Recent mobile and wearable electroencephalogram (EEG)-sensing technologies have been demonstrated to be effective for measuring rapid changes of spatio-spectral EEG correlates of brain and cognitive functions of interest with more ecologically natural settings. However, commercial EEG products are available commonly with a fixed headset in terms of the number of electrodes and their locations to the scalp practically constrains their generalizability for different demands of EEG and brain-computer interface (BCI) study. While most progress focused on innovation of sensing hardware and conductive electrodes, less effort has been done to renovate mechanical structures of an EEG headset. Recently, an electrode-holder assembly infrastructure was designed to be capable of unlimitedly (re)assembling a desired n-channel electrode headset through a set of primary elements (i.e., LEGO-like headset). The present work empirically demonstrated one of its advantage regarding coordinating the homogeneous or heterogeneous sensors covering the target regions of the brain. Towards this objective, an 8-channel LEGO headset was assembled to conduct a simultaneous event-related potential (ERP) recording of the wet- and dry-electrode EEG systems and testify their signal quality during standing still versus treadmill walking. The results showed that both systems returned a comparable P300 signal-to-noise ratio (SNR) for standing, yet the dry system was more susceptible to the movement artifacts during slow walking. The LEGO headset infrastructure facilitates a desired benchmark study, e.g., comparing the signal quality of different electrodes on non-stationary subjects conducted in this work, or a specific EEG and BCI application.

Wireless User-Generic Ear EEG.

In the past few years it has been demonstrated that electroencephalography (EEG) can be recorded from inside the ear (in-ear EEG). To open the door to low-profile earpieces as wearable brain-computer interfaces (BCIs), this work presents a practical in-ear EEG device based on multiple dry electrodes, a user-generic design, and a lightweight wireless interface for streaming data and device programming. The earpiece is designed for improved ear canal contact across a wide population of users and is fabricated in a low-cost and scalable manufacturing process based on standard techniques such as vacuum forming, plasma-treatment, and spray coating. A 2.5×2.5cm2 wireless recording module is designed to record and stream data wirelessly to a host computer. Performance was evaluated on three human subjects over three months and compared with clinical-grade wet scalp EEG recordings. Recordings of spontaneous and evoked physiological signals, eye-blinks, alpha rhythm, and the auditory steady-state response (ASSR), are presented. This is the first wireless in-ear EEG to our knowledge to incorporate a dry multielectrode, user-generic design. The user-generic ear EEG recorded a mean alpha modulation of 2.17, outperforming the state-of-the-art in dry electrode in-ear EEG systems.

An EEG system to detect brain signals from multiple adult zebrafish

Zebrafish has been widely used as an experimental animal in many biological processes to study brain functions, neurological disorders and drug toxicity. Electroencephalogram (EEG), which directly measures brain activities, has been used to diagnose and study neurological disorders. Previous studies have recorded EEG signals from adult zebrafish, but the recordings have been only possible from a single individual. In this study, we developed a system to record EEG of multiple adult zebrafish simultaneously. To secure multiple zebrafish in a stable condition for a certain time, perfusion and recording systems were mechanically separated, which allowed effective immobilization of the fish. We recorded EEG signals from pentylenetetrazole (PTZ)-induced seizure models and valproic acid (VPA)-treated models to demonstrate the performance of the developed system in validation of the effect of anti-epileptic drugs. The developed system effectively measured individual EEG signals from more than three zebrafish out of four simultaneously on average. Seizure induction by PTZ and seizure suppression by VPA were successfully detected through EEG recording. The scheme of the EEG recording system developed in this study has a potential for expansion and could be further applied to mass drug screening in a short time.

Beyond BCI—Validating a wireless, consumer-grade EEG headset against a medical-grade system for evaluating EEG effects of a test anxiety intervention in school

Educational neuroscience is an emerging interdisciplinary field. However, the use of neuroimaging techniques and tools such as electroencephalography (EEG) in school-based interventions and research is limited, largely due to the high costs and physical constraints of conventional research- or medical-grade equipment. Neuroimaging and electrophysiological data can provide useful evidence to validate the efficacy of interventions. The present study explores the utility of lightweight, affordable, and easy-to-set-up EEG systems for use in school-based research with children. Specifically, we examine the effects of a deep-breathing-for-test-anxiety intervention on brain electrical activity during a flanker distractor interference task in eleven-year olds, comparing the pattern of results observed using a consumer-grade EEG system (Emotiv EPOC+) against that obtained using a medical-grade EEG system (Neurostyle). Behavioral, EEG, and respiratory data was obtained from Primary 5 students (N = 45; Mage = 10.88, SD = 0.33), split into Emotiv and Neurostyle groups. The aim of the study was two-fold: to examine the effects of deep breathing on neurophysiological and behavioral correlates of inhibitory control of attention in children, and to understand the affordances and limitations of the Emotiv EPOC+ system for school-based research with children. Results from power spectral analyses suggest that deep breathing may enhance attentional control on a neural level by modulating brain electrical activity on several frequencies. Despite limitations, the consumer-grade EEG system appears to be capable of detecting some degree of power spectral differences associated with intervention effects.

Dynamic Changes in EEG Power Spectral Densities During NIH-Toolbox Flanker, Dimensional Change Card Sort Test and Episodic Memory Tests in Young Adults.

Much is known about electroencephalograph (EEG) patterns during sleep, but until recently, it was difficult to study EEG patterns during conscious, awake behavior. Technological advances such as powerful wireless EEG systems have led to a renewed interest in EEG as a clinical and research tool for studying real-time changes in the brain. We report here the first normative study of EEG activity while healthy young adults completed a series of cognitive tests recently published by the National Institutes of Health Toolbox Cognitive Battery (NIH-TCB), a commonly-used standardized measure of cognition primarily used in clinical populations. In this preliminary study using a wireless EEG system, we examined power spectral density (PSD) in four EEG frequency bands. During baseline and cognitive testing, PSD activity for the lower frequency bands (theta and alpha) was greater, relative to the higher frequency bands (beta and gamma), suggesting participants were relaxed and mentally alert. Alpha, beta and gamma activity was increased during a memory test compared to two other, less demanding executive function tests. Gamma activity was also inversely correlated with performance on the memory test, consistent with the neural efficiency hypothesis which proposes that better cognitive performance may link with lower cortical energy consumption. In summary, our study suggests that cognitive performance is related to the dynamics of EEG activity in a normative young adult population.

High-Density Adaptive Ten Ten: Proposal for Electrode Nomenclature for High-Density EEG.

High-density EEG (HD-EEG) systems and electrical source imaging techniques have revolutionized our ability to assess the potential sources of epileptiform activity and other EEG features. Nonetheless, clinical use of HD-EEG is hampered by the lack of a standardized electrode nomenclature system and the inherent difficulties encountered in visually reviewing recordings. Inefficient visual review of HD-EEG remains a major barrier to incorporating these techniques into routine clinical care. Extension of the 10-10 is first defined by the addition of 2 reference curves: the -10% and -20% axial reference curves. Electrode positions over the face are named based on facial bony structures (N = nasion, Z = zygomatic prominence, M = mandible) and over the back of the head on posterior landmarks (I = inion, S = subinion, B = Base). Then, following the 10% incremental distance rule, we define additional electrode positions. Electrodes with nonstandard positions are clustered around the closest 10-10 electrode, deemed their cardinal point. The 256-electrode Geodesic Sensor Net mapped to 96 of the 120 extended 10-10 cardinal electrodes. Electrode position nomenclature that builds upon the international standard 10-10 system allows electroencephalographers to identify spatial areas of interest in HD-EEG relative to positions in routine use. A standard viewing montage for HD-EEG and its application with electrical source imaging boost efficiency when reviewing data and improve accuracy in recognizing epileptiform discharges. Additionally, our proposed system is not limited to a specific HD-EEG system, electrode count, or electrode layout.

Findings about LORETA Applied to High-Density EEG—A Review

Electroencephalography (EEG) is a non-invasive diagnostic technique for recording brain electric activity. The EEG source localization has been an area of research widely explored during the last decades because it provides helpful information about brain physiology and abnormalities. Source localization consists in solving the so-called EEG inverse problem. Over the years, one of the most employed method for solving it has been LORETA (Low Resolution Electromagnetic Tomography). In particular, in this review, we focused on the findings about the LORETA family algorithms applied to high-density EEGs (HD-EEGs), used for improving the low spatial resolution deriving from the traditional EEG systems. The results were classified according to their clinical application and some aspects arisen from the analyzed papers were discussed. Finally, suggestions were provided for future improvement. In this way, the combination of LORETA with HD-EEGs could become an even more valuable tool for noninvasive clinical evaluation in the field of applied neuroscience.

Prediction of gait intention from pre-movement EEG signals: a feasibility study

BackgroundPrediction of Gait intention from pre-movement Electroencephalography (EEG) signals is a vital step in developing a real-time Brain-computer Interface (BCI) for a proper neuro-rehabilitation system. In that respect, this paper investigates the feasibility of a fully predictive methodology to detect the intention to start and stop a gait cycle by utilizing EEG signals obtained before the event occurrence.MethodsAn eight-channel, custom-made, EEG system with electrodes placed around the sensorimotor cortex was used to acquire EEG data from six healthy subjects and two amputees. A discrete wavelet transform-based method was employed to capture event related information in alpha and beta bands in the time-frequency domain. The Hjorth parameters, namely activity, mobility, and complexity, were extracted as features while a two-sample unpaired Wilcoxon test was used to get rid of redundant features for better classification accuracy. The feature set thus obtained was then used to classify between ’walk vs. stop’ and ’rest vs. start’ classes using support vector machine (SVM) classifier with RBF kernel in a ten-fold cross-validation scheme.ResultsUsing a fully predictive intention detection system, 76.41±4.47% accuracy, 72.85±7.48% sensitivity, and 79.93±5.50% specificity were achieved for ’rest vs. start’ classification. While for ’walk vs. stop’ classification, the obtained mean accuracy, sensitivity, and specificity were 74.12±4.12%, 70.24±6.45%, and 77.78±7.01% respectively. Overall average True Positive Rate achieved by this methodology was 72.06±8.27% with 1.45 False Positives/min.ConclusionExtensive simulations and resulting classification results show that it is possible to achieve statistically similar intention detection accuracy using either only pre-movement EEG features or trans-movement EEG features. The classifier performance shows the potential of the proposed methodology to predict human movement intention exclusively from the pre-movement EEG signal to be applied in real-life prosthetic and neuro-rehabilitation systems.

Electric source imaging for presurgical epilepsy evaluation: current status and future prospects

ABSTRACTIntroduction: Electric source imaging (ESI) refers to the estimation of the cerebral sources of electric signals recorded at the head surface using electroencephalography (EEG). Thanks to the availability of EEG systems with high numbers of electrodes and to progress in software to analyze the signals they collect, ESI can be applied to epilepsy-related pathological EEG signals like interictal spikes and seizures.Areas covered: In this narrative review, we discuss selected original research articles on the use of ESI in epilepsy patients considered for surgery. Epilepsy-related activity can be localized accurately using ESI, as established by comparison to the gold standards of intracranial EEG and seizure control following epilepsy surgery. The information brought by ESI complements successfully that of other techniques like magnetic resonance imaging and positron-emission tomography, and is clinically relevant to patient management.Expert opinion: EEG is a readily available technique to measure brain activity in real time. Given its accuracy and usefulness, ESI should become part of the routine practice of clinical neurophysiology laboratories and epilepsy centers in the presurgical management of epilepsy patients.

Using high density EEG to assess TMS treatment in patients with schizophrenia.

We present preliminary results from the ongoing study entitled “Icelandic AVH-TMS” which aim is to study the effectiveness of repetitive transcranial magnetic stimulation (rTMS) treatment for patients with schizophrenia and with persistent auditory verbal hallucinations (AVH) using symptoms and psychometric scales and high-density EEG system (256 channels). The aim of the present work was to describe cortical topography of the auditory evoked responses like P50 and N100-P300 complex in healthy participants and patients with schizophrenia and to define a robust methodology of signal quantification using dense-array EEG. Preliminary data is shown for three healthy participants and three patients in baseline conditions and for two patients we show the results recorded before and after 10 days rTMS treatment. Our results show differences in sensory gating (P50 suppresion) and a stronger N100-P300 response to rare audio stimulus after the treatment. Moreover we show the value of assessing brain electrical activity from high-density EEG (256 channels) analyzing the results in different regions of interest. However, it is premature and hazardous to assume that rTMS treatment effectiveness in patients with AVH can be assessed using P50 suppression ratio.

Slow wave oscillations in Schizophrenia First-Degree Relatives: A confirmatory analysis and feasibility study on slow wave traveling

Abnormal sleep oscillations have recently been proposed as endophenotypes of schizophrenia. However, optimization of methodological approaches is still necessary to standardize analyses of their microstructural characteristics. Additionally, some relevant features of these oscillations remain unexplored in pathological conditions. Among others, slow wave traveling is a promising proxy for diurnal processes of brain connectivity and excitability. The study of slow oscillations propagation appears particularly relevant when schizophrenia is conceptualized as a dys-connectivity syndrome. Given the rising knowledge on the neurobiological mechanisms underlying slow wave traveling, this measure might offer substantial advantages over other approaches in investigating brain connectivity.Herein we: 1) confirm the stability of our previous findings on slow waves and sleep spindles in FDRs using different automated algorithms, and 2) report the dynamics of slow wave traveling in FDRs of Schizophrenia patients. A 256-channel, high-density EEG system was employed to record a whole night of sleep of 16 FDRs and 16 age- and gender-matched control subjects. A recently developed, open source toolbox was used for slow wave visualization and detection. Slow waves were confirmed to be significantly smaller in FDRs compared to the control group. Additionally, several traveling parameters were analyzed. Traveled distances were found to be significantly reduced in FDRs, whereas origins showed a different topographical pattern of distribution from control subjects. In contrast, local speed did not differ between groups.Overall, these results suggest that slow wave traveling might be a viable method to study pathological conditions interfering with brain connectivity.