EEG Electrodes Research Articles

A review of Graph Neural Networks for Electroencephalography data analysis

Electroencephalography (EEG) sensors are flexible and non-invasive sensoring devices for the measurement of electrical brain activity which is extensively used in some areas of clinical practice and psychological/psychiatric research, such as epilepsy, sleep, emotion, and brain computer interfaces. Although EEG sensor do not provide actual brain localizations of the activity sources, they allow to study brain functional connectivity. In this paper we review current application of a specific family of computational methods, the Graph Neural Networks (GNN) to the analysis of EEG data. GNNs appear to be well suited to EEG data modeling as they deal with signals whose domain is defined by a graph instead of a regular lattice in Euclidean space. Readings of EEG electrodes fall in this category, hence the increasing research activity on the application of GNNs to EEG data.

Prediction of Gait Kinematics and Kinetics: A Systematic Review of EMG and EEG Signal Use and Their Contribution to Prediction Accuracy.

Human-machine interfaces hold promise in enhancing rehabilitation by predicting and responding to subjects' movement intent. In gait rehabilitation, neural network architectures utilize lower-limb muscle and brain activity to predict continuous kinematics and kinetics during stepping and walking. This systematic review, spanning five databases, assessed 16 papers meeting inclusion criteria. Studies predicted lower-limb kinematics and kinetics using electroencephalograms (EEGs), electromyograms (EMGs), or a combination with kinematic data and anthropological parameters. Long short-term memory (LSTM) and convolutional neural network (CNN) tools demonstrated highest accuracies. EEG focused on joint angles, while EMG predicted moments and torque joints. Useful EEG electrode locations included C3, C4, Cz, P3, F4, and F8. Vastus Lateralis, Rectus Femoris, and Gastrocnemius were the most commonly accessed muscles for kinematic and kinetic prediction using EMGs. No studies combining EEGs and EMGs to predict lower-limb kinematics and kinetics during stepping or walking were found, suggesting a potential avenue for future development in this technology.

Convolutional gated recurrent unit-driven multidimensional dynamic graph neural network for subject-independent emotion recognition

Electroencephalogram (EEG) could directly reflect human brain activities. Recently, EEG-based emotion recognition technology has attracted widespread attention. However, although most of the existing methods can extract temporal and spatial features of EEG signals, the extraction process of the time-series information and spatial features is separate from each other. Some spatial features may be lost when extracting time-series information, and vice versa. Additionally, the non-Euclidean representations between the EEG electrodes cannot be ignored. In response to these issues, we construct a convolutional gated recurrent unit-driven multidimensional dynamic graph neural network (CGRU-MDGN), which mines its local spatial characteristics of EEG signals while learning the temporal information and captures non-Euclidean spatial features between EEG channels to classify emotions more accurately. Specifically, we substitute the fully connected layers of the GRU with convolution layers, which can take into account both the local spatial features and time-series information of EEG signals. Next, multiple non-Euclidean spatial representations are generated using multidimensional dynamic graph convolution. Furthermore, the characteristics of different dimensions or views are merged through the adaptive-weighted summation technique to realize the maximized feature extraction as much as possible. Comprehensive experiments reveal that the constructed CGRU-MDGN could achieve competitive recognition results on subject-independent emotion recognition tasks.

Ketogenic Diet Affects Sleep Architecture in C57BL/6J Wild Type and Fragile X Mice.

Nearly half of children with fragile X syndrome experience sleep problems including trouble falling asleep and frequent nighttime awakenings. The goals here were to assess sleep-wake cycles in mice in response to Fmr1 genotype and a dietary intervention that reduces hyperactivity. Electroencephalography (EEG) results were compared with published rest-activity patterns to determine if actigraphy is a viable surrogate for sleep EEG. Specifically, sleep-wake patterns in adult wild type and Fmr1KO littermate mice were recorded after EEG electrode implantation and the recordings manually scored for vigilance states. The data indicated that Fmr1KO mice exhibited sleep-wake patterns similar to wild type littermates when maintained on a control purified ingredient diet. Treatment with a high-fat, low-carbohydrate ketogenic diet increased the percentage of non-rapid eye movement (NREM) sleep in both wild type and Fmr1KO mice during the dark cycle, which corresponded to decreased activity levels. Treatment with a ketogenic diet flattened diurnal sleep periodicity in both wild type and Fmr1KO mice. Differences in several sleep microstructure outcomes (number and length of sleep and wake bouts) supported the altered sleep states in response to a ketogenic diet and were correlated with altered rest-activity cycles. While actigraphy may be a less expensive, reduced labor surrogate for sleep EEG during the dark cycle, daytime resting in mice did not correlate with EEG sleep states.

Disruption of EEG resting state functional connectivity in patients with focal epilepsy

BackgroundEpilepsy is a network disease and EEG could be used to evaluate dynamic inter-regional connectivity. The aim of the current study is to explore disruption of resting state EEG functional connectivity in focal epilepsy using coherence and phase lag degree. This cross-sectional study included 30 patients with focal epilepsy and 30 matched healthy controls. One to two minutes of EEG segmented into 2-s epochs during awake eye-closed state were analyzed using fast Fourier transform to yield four frequency bands: delta, theta, alpha and beta. Coherence and phase lag degree were computed between each pair of 19 EEG electrodes and were assessed at the intra-hemispheric (frontal–parietal and frontal–temporal) and inter-hemispheric (frontal, temporal and parietal) levels. The frequency of interictal epileptiform discharges (IEDs) was calculated from a 60-min EEG recording session.ResultsCompared to healthy controls, patients had lower theta coherence at left frontal–parietal (P = 0.017), lower delta coherence at inter-frontal (P = 0.045), higher theta phase lag at right frontal–parietal (P = 0.01) and lower delta phase lag at inter-temporal (P = 0.046) levels. Patients with left-sided epilepsy had lower theta coherence at left frontal–parietal (P = 0.026), higher theta phase lag at right frontal–parietal (P < 0.001), higher delta phase lag at right frontal–temporal (P = 0.036) and higher theta phase lag at inter-parietal (P = 0.028) levels. The frequency of IEDs correlated with phase lag of delta (P = 0.036, r = 0.406) and theta (P = 0.005, r = 0.513).ConclusionsPatients with focal epilepsy had significant interictal functional connectivity disruption detected by coherence and phase lag degree of delta and theta waves and correlated with frequency of IEDs.

Fast ripples reflect increased excitability that primes epileptiform spikes.

The neuronal circuit disturbances that drive inter-ictal and ictal epileptiform discharges remain elusive. Using a combination of extra-operative macro-electrode and micro-electrode inter-ictal recordings in six pre-surgical patients during non-rapid eye movement sleep, we found that, exclusively in the seizure onset zone, fast ripples (200-600 Hz), but not ripples (80-200 Hz), frequently occur <300 ms before an inter-ictal intra-cranial EEG spike with a probability exceeding chance (bootstrapping, P < 1e-5). Such fast ripple events are associated with higher spectral power (P < 1e-10) and correlated with more vigorous neuronal firing than solitary fast ripple (generalized linear mixed-effects model, P < 1e-9). During the intra-cranial EEG spike that follows a fast ripple, action potential firing is lower than during an intra-cranial EEG spike alone (generalized linear mixed-effects model, P < 0.05), reflecting an inhibitory restraint of intra-cranial EEG spike initiation. In contrast, ripples do not appear to prime epileptiform spikes. We next investigated the clinical significance of pre-spike fast ripple in a separate cohort of 23 patients implanted with stereo EEG electrodes, who underwent resections. In non-rapid eye movement sleep recordings, sites containing a high proportion of fast ripple preceding intra-cranial EEG spikes correlate with brain areas where seizures begin more than solitary fast ripple (P < 1e-5). Despite this correlation, removal of these sites does not guarantee seizure freedom. These results are consistent with the hypothesis that fast ripple preceding EEG spikes reflect an increase in local excitability that primes EEG spike discharges preferentially in the seizure onset zone and that epileptogenic brain regions are necessary, but not sufficient, for initiating inter-ictal epileptiform discharges.

Correlation study of brain-derived neurotrophic factor, EEG γ activity and cognitive function in first-episode schizophrenia

BackgroundSchizophrenia is characterised by neurotrophic, neuroelectrophysiological and cognitive dysfunction. However, there exists a paucity of research examining the association between serum brain-derived neurotrophic factor (BDNF) concentration, resting electroencephalogram (EEG) gamma activity, and cognitive impairment in individuals diagnosed with schizophrenia. MethodsIn this study, 87 first-episode schizophrenia patients and 75 healthy controls were assessed. Measurements were conducted to determine the levels of BDNF, resting EEG γ-activity at left and right frontal pole EEG electrodes respectively (FP1/FP2) leads, and cognitive function as assessed by the Measurement and Treatment Research to Improve Cognition in Schizophrenia MATRICS Consensus Cognitive Battery (MCCB). Comparisons were made between the patient group and the control group, revealing lower BDNF levels, lower T-scores for 7 MCCB cognitive items, and higher EEG γ-activity among patients when compared to controls. ResultsAccording to the correlation analysis, there were significant associations observed in the patient group. BDNF levels were found to be correlated with EEG γ activity as well as T-scores of speed of processing (SoP), verbal learning (VeL), and reasoning problem-solving (RPS). Moreover, EEG γ activity showed an association with both the total score of the Positive and Negative Syndrome Scale (PANSS) and T-score of SoP. These findings suggest a potential relationship between BDNF levels, EEG γ activity, cognitive domains, and clinical symptoms in individuals with first-episode schizophrenia. ConclusionsIn conclusion, our findings demonstrate the coexistence of neurobiochemical and electrophysiological abnormalities alongside cognitive dysfunction during the early stages of schizophrenia. These findings provide valuable insights into the mechanism of cognitive impairment in schizophrenia. By highlighting the simultaneous occurrence of these factors, our study contributes to a better understanding of the complex nature of schizophrenia and emphasizes the importance of studying its cognitive aspects.

Emotion Recognition from Electroencephalogram Signals based on Deep Neural Networks

Emotion recognition using deep learning methods through electroencephalogram (EEG) analysis has marked significant progress. Nevertheless, the complexities and time-intensive nature of EEG analysis present challenges. This study proposes an efficient EEG analysis method that foregoes feature extraction and sliding windows, instead employing one-dimensional Neural Networks for emotion classification. The analysis utilizes EEG signals from the Database for Emotion Analysis using Physiological Signals (DEAP) and focuses on thirteen EEG electrode positions closely associated with emotion changes. Three distinct Neural Models are explored for emotion classification: two Convolutional Neural Networks (CNN) and a combined approach using Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM). Additionally, two emotion labels are considered: four emotional ranges encompassing low arousal and low valence (LALV), low arousal and high valence (LAHV), high arousal and high valence (HAHV), and high arousal and low valence (HALV); and high valence (HV) and low valence (LV). Results demonstrate CNN_1 achieving an average accuracy of 97.7% for classifying four emotional ranges, CNN_2 with 97.1%, and CNN-LSTM reaching an impressive 99.5%. Notably, in classifying HV and LV labels, our methods attained remarkable accuracies of 100%, 98.8%, and 99.7% for CNN_1, CNN_2, and CNN-LSTM, respectively. The performance of our models surpasses that of previously reported studies, showcasing their potential as highly effective classifiers for emotion recognition using EEG signals.

Advancement of the Electrochemical Performance of Stainless-Steel EEG Electrodes by Functionalizing with Carbon Nanotubes and Antibiofouling Molecules

Understanding brain function and neurological disorders are essential to develop therapeutic strategies such as neuropharmacological medications and implantable biomedical tools to treat brain-related disorders. Implantable neural electrodes can deliver in-depth knowledge of brain activity and disorders by recording real-time brain signals. Epilepsy is an electrophysiological brain disorder caused by genetic factors or a brain injury. Electroencephalography (EEG) is a standard technique that provides more specific information about epilepsy. Both invasive and non-invasive EEG electrodes are used to study epilepsy and possess high temporal resolution. Invasive EEG electrodes are useful for localizing the brain's regions where epilepsy arises. EEG electrodes are mainly metal-based electrodes. Stainless steel, platinum, gold, tin, and silver are commonly used metals. Electrode material plays a critical role in the accuracy of the brain signal measurements and consistent performance in chronic applications. Stainless steel EEG electrodes are successfully used in EEG signal monitoring in vivo; however, stainless steel can undergo chloride-induced corrosion and biofouling over time. Corrosion and biofouling can negatively affect the impedance of the EEG electrodes resulting in poor signal recording and inconsistent performance. The current study reports the advancement of the performance of stainless-steel EEG electrodes by functionalizing with vertically aligned carbon nanotubes (CNTs) and zwitterionic phenyl phosphorylcholine (PPC) molecules. CNTs are conductive and biocompatible nanomaterial which have been successfully used for neural interfacing applications. The results show that the electrochemical performance of the EEG electrodes was significantly improved after functionalization with vertically aligned CNTs. Electrochemically grafting of zwitterionic PPC molecules onto CNTs further improved electrochemical performance and introduced anti-biofouling properties.

Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.

Deep learning (DL) is emerging as a successful technique for automatic detection and differentiation of spontaneous seizures that may otherwise be missed or misclassified. Herein, we propose a system architecture based on top-performing DL models for binary and multigroup classifications with the non-overlapping window technique, which we tested on the TUSZ dataset. The system accurately detects seizure episodes (87.7% Sn, 91.16% Sp) and carefully distinguishes eight seizure types (95-100% Acc). An increase in EEG sampling rate from 50 to 250 Hz boosted model performance: the precision of seizure detection rose by 5%, and seizure differentiation by 7%. A low sampling rate is a reasonable solution for training reliable models with EEG data. Decreasing the number of EEG electrodes from 21 to 8 did not affect seizure detection but worsened seizure differentiation significantly: 98.24 ± 0.17 vs. 85.14 ± 3.14% recall. In detecting epileptic episodes, all electrodes provided equally informative input, but in seizure differentiation, their informative value varied. We improved model explainability with interpretable ML. Activation maximization highlighted the presence of EEG patterns specific to eight seizure types. Cortical projection of epileptic sources depicted differences between generalized and focal seizures. Interpretable ML techniques confirmed that our system recognizes biologically meaningful features as indicators of epileptic activity in EEG.

Neural modulation enhancement using connectivity-based EEG neurofeedback with simultaneous fMRI for emotion regulation

Emotion regulation plays a key role in human behavior and overall well-being. Neurofeedback is a non-invasive self-brain training technique used for emotion regulation to enhance brain function and treatment of mental disorders through behavioral changes. Previous neurofeedback research often focused on using activity from a single brain region as measured by fMRI or power from one or two EEG electrodes. In a new study, we employed connectivity-based EEG neurofeedback through recalling positive autobiographical memories and simultaneous fMRI to upregulate positive emotion. In our novel approach, the feedback was determined by the coherence of EEG electrodes rather than the power of one or two electrodes. We compared the efficiency of this connectivity-based neurofeedback to traditional activity-based neurofeedback through multiple experiments. The results showed that connectivity-based neurofeedback effectively improved BOLD signal change and connectivity in key emotion regulation regions such as the amygdala, thalamus, and insula, and increased EEG frontal asymmetry, which is a biomarker for emotion regulation and treatment of mental disorders such as PTSD, anxiety, and depression and coherence among EEG channels. The psychometric evaluations conducted both before and after the neurofeedback experiments revealed that participants demonstrated improvements in enhancing positive emotions and reducing negative emotions when utilizing connectivity-based neurofeedback, as compared to traditional activity-based and sham neurofeedback approaches. These findings suggest that connectivity-based neurofeedback may be a superior method for regulating emotions and could be a useful alternative therapy for mental disorders, providing individuals with greater control over their brain and mental functions.

Robot-assisted transcerebellar stereotactic approach to the posterior fossa in pediatric patients: a technical note.

During the last decade, there has been renewed interest in stereotactic approaches to diffuse intrinsic pontine gliomas (DIPGs) in children, due to the development of new concepts in molecular biology and management, and subsequent need for tissue sampling. Stereotactic frame-based and robot-assisted techniques are associated with reduced target error and have been incorporated into standard practice at our institution. Four children (age 2-7years) underwent a robot-assisted frame-based transcerebellar approach using the Leksell G frame coupled with Renishaw's neuromate® stereotactic robot. The procedures included 3 biopsies (two brainstem tumors and one cerebellar hemispheric lesion) and 1 depth electrode implantation into a low-grade tumor remnant (ganglioglioma) of the middle cerebellar peduncle causing drug-resistant epilepsy in a young girl. Targeting was based on MRI, and in one case, 18F-FET-PET was coregistered to MRI to improve sampling accuracy. The frame was applied 180° rotated compared to standard orientation, and patients were positioned prone during surgery and stereotactic preoperative CT scan. Postoperative CT scan ruled out complications and was coregistered to preoperative MRI to check the target accuracy. No complications occurred, and targeting was accurate in all cases. All tissue samplings provided proper histology; depth electrode EEG exploration was diagnostic and led subsequent resective surgery. According to our experience, the transcerebellar frame-based robotic stereotactic approach to the cerebellum and the brainstem is feasible, safe, and effective even in young children.

Observers for Phenomenological Models of Epileptic Seizures.

Progress towards effective treatment of epileptic seizures has seen much improvement in the past decade. In particular, the emergence of phenomenological models of epileptic seizures specifically designed to capture the electrical seizure dynamics in the Epileptor model is inspiring new approaches to predicting and controlling seizures. These new models present in various forms and contain important but unmeasurable variables that control the occurrence of seizures. These models have been used mostly as nodes in large networks to study the complex brain behaviour of seizures. In order to use this model for the purposes of seizure forecasting or to control seizures through deep brain stimulation, the states of the model will need to be estimated. Although devices such as EEG electrodes can be related to some of the states of the model, most remain unmeasured and would require an observer (as defined in control theory) for their estimation. Additionally, we would like to consider the case for large nodes of systems where the number of electrodes is far smaller than the number of nodes being estimated. In this paper, we provide methods towards obtaining the full states of these phenomenological models using nonlinear observers. In particular, we explore the effectiveness of the Extended Kalman Filter for small networks of nodes of a smoothed sixth order Epileptor model. We show that observer design is possible for this family of systems and identify the difficulties in doing so.Clinical relevance-The methods presented here can be applied with an individual epileptic patient's EEG to reveal previously hidden biomarkers of epilepsy for seizure forecasting.

Novel dry EEG electrode with composite filler of PEDOT:PSS and carbon particles.

We have developed a novel composite filler with Poly(3,4-ethylenedioxythiophene) : poly(styrenesulfonic acid) (PEDOT:PSS), a biocompatible organic conductive polymer, adsorbed on carbon particles for biological electrodes. This composite filler enables to fabricate high-performance biological electrodes simply by adding it to resin in the same way as conventional conductive fillers. The fabricated electrodes achieve ion exchange properties similar to those of PEDOT:PSS polymers and therefore low skin and electrode contact impedance. Electroencephalogram (EEG) measurements show that these electrodes capture various brain activities and exhibit high correlation (≥ 0.9) to commercially available wet and AgCl electrodes. Additionally, each electrode can be molded into various shapes and structures while retaining its electrode characteristics. Therefore, the proposed electrode is promising for EEG measurement, which requires high comfort and signal quality.

Comparison of Sub-Scalp EEG and Endovascular Stent-Electrode Array for Visual Evoked Potential Brain-Computer Interface.

Brain-computer interfaces (BCI) have the potential to improve the quality of life for persons with paralysis. Sub-scalp EEG provides an alternative BCI signal acquisition method that compromises between the limitations of traditional EEG systems and the risks associated with intracranial electrodes, and has shown promise in long-term seizure monitoring. However, sub-scalp EEG has not yet been assessed for suitability in BCI applications. This study presents a preliminary comparison of visual evoked potentials (VEPs) recorded using sub-scalp and endovascular stent electrodes in a sheep. Sub-scalp electrodes recorded comparable VEP amplitude, signal-to-noise ratio and bandwidth to the stent electrodes.Clinical relevance-This is the first study to report a comparision between sub-scalp and stent electrode array signals. The use of sub-scalp EEG electrodes may aid in the long-term use of brain-computer interfaces.

Alpha and theta oscillations during the cognitive reappraisal of aversive pictures: A spatio-temporal qEEG investigation

ContextEmotion regulation is a set of processes responsible for controlling, evaluating and adjusting reactions to achieve a goal. Results derived from magnetic resonance imaging agreed on the involvement of frontal and limbic structures in this process. Findings using cognition and physiology interactions are still scarce but suggest a role of alpha rhythm in emotional induction and for theta in regulation. Objectives and hypothesesOur goal was to investigate alpha and theta rhythm during the reappraisal of aversive stimuli. We hypothesized that an implication of alpha rhythm in emotional induction only and an increase in prefrontal theta rhythm positively correlated with successful regulation. MethodTwenty-four healthy participants were recorded with 64 EEG electrodes while asked to watch or reappraise negative pictures passively. Theta and alpha rhythms were compared across maintain, decrease and increase regulation conditions, and a source localization estimated the generators. ResultsTheta activity was consistently higher in the upregulation than in the maintenance condition (p = .04) for the entire control period, but mainly at the beginning of regulation (1–3 s) for low-theta and later (5–7 s) for high-theta. Moreover, our results confirm that a low-theta generator correlated with mainly the middle frontal gyrus and the anterior dorsal cingulate cortex during upregulation. Theta was sensitive to emotion upregulation, whereas the alpha oscillation was non-sensitive to emotion induction and regulation. ConclusionTheta rhythm was involved explicitly in emotion upregulation processes that occur at a definite time during reappraisal, whereas the alpha rhythm was not altered by emotion induction and regulation.

Low impedance electrodes improve detection of high frequency oscillations in the intracranial EEG

ObjectiveEpileptic fast ripple oscillations (FR, 250–500 Hz) indicate epileptogenic tissue with high specificity. However, their low amplitude makes detection demanding against noise. Since thermal noise is reduced by low impedance electrodes (LoZ), we investigate here whether this noise reduction is relevant in the FR frequency range. MethodsWe analyzed intracranial electrocorticography during neurosurgery of 10 patients where a low impedance electrode was compared to a standard electrode (HiZ) with equal surface area during stimulation of the somatosensory evoked potential, which evokes a robust response in the FR frequency range. To estimate the noise level, we computed the difference between sweep 2n and sweep 2n + 1 for all sweeps. ResultsThe power spectral density of the noise spectrum improved for the LoZ over all frequencies. In the FR range, the median noise level improved from HiZ (0.153 µV) to LoZ (0.089 µV). For evoked FR, the detection rate improved (91% for HiZ vs. 100% for LoZ). ConclusionsLow impedance electrodes for intracranial EEG reduce noise in the FR frequency range and may thereby improve FR detection. SignificanceImproving the measurement chain may enhance the diagnostic value of FR as biomarkers for epileptogenic tissue.

Curriculum Innovations: Developing an EEG Workshop for Hands-on Training in Electroencephalography During Resident Rotation in Clinical Neurophysiology

ABSTRACT A 3-hour hands-on EEG workshop was conducted as a part of resident rotation in clinical neurophysiology to determine its feasibility and educational value for neurology residents, medical students, and EEG technologists, with the learning objectives of motivating interest in clinical electroencephalography, enhancing ability to recognize a normal EEG and identify physiological and non-physiological artifacts, and enhancing teaching, organizational and communication skills of EEG technologists. The workshop included a demonstration of EEG electrode placement according to the 10–20 System on a resident volunteer by the EEG technologist. Activation procedures (photic stimulation and hyperventilation) were demonstrated to the residents during the 25-minute recording. A board-certified epileptologist later reviewed the record with the residents. Resident and EEG technologist feedback on the workshop was obtained via non-formal e-mail request. All residents rated the workshop highly and found it more engaging and motivating than the didactic EEG lecture in which basics of EEG recording are reviewed. The residents gained improved awareness and appreciation of the role of the technologist and the skill and time involved in obtaining an EEG record. The EEG technologist appreciated the face-to-face interaction with the residents and felt part of the patient care team. A hands-on EEG workshop as a part of resident clinical neurophysiology rotation is feasible, effective, and motivating in teaching residents and medical students the basis of EEG recording and recognition of common physiological and non-physiological artifacts in a normal record.

Deep EEG Superresolution via Correlating Brain Structural and Functional Connectivities.

Electroencephalogram (EEG) excels in portraying rapid neural dynamics at the level of milliseconds, but its spatial resolution has often been lagging behind the increasing demands in neuroscience research or subject to limitations imposed by emerging neuroengineering scenarios, especially those centering on consumer EEG devices. Current superresolution (SR) methods generally do not suffice in the reconstruction of high-resolution (HR) EEG as it remains a grand challenge to properly handle the connection relationship amongst EEG electrodes (channels) and the intensive individuality of subjects. This study proposes a deep EEG SR framework correlating brain structural and functional connectivities (Deep-EEGSR), which consists of a compact convolutional network and an auxiliary fully connected network for filter generation (FGN). Deep-EEGSR applies graph convolution adapting to the structural connectivity amongst EEG channels when coding SR EEG. Sample-specific dynamic convolution is designed with filter parameters adjusted by FGN conforming to functional connectivity of intensive subject individuality. Overall, Deep-EEGSR operates on low-resolution (LR) EEG and reconstructs the corresponding HR acquisitions through an end-to-end SR course. The experimental results on three EEG datasets (autism spectrum disorder, emotion, and motor imagery) indicate that: 1) Deep-EEGSR significantly outperforms the state-of-the-art counterparts with normalized mean squared error (NMSE) decreased by 1% - 6% and the improvement of signal-to-noise ratio (SNR) up to 1.2 dB and 2) the SR EEG manifests superiority to the LR alternative in ASD discrimination and spatial localization of typical ASD EEG characteristics, and this superiority even increases with the scale of SR.

Effects of the T-type calcium channel CaV3.2 R1584P mutation on absence seizure susceptibility in GAERS and NEC congenic rats models

RationaleLow-voltage-activated or T-type Ca2+ channels play a key role in the generation of seizures in absence epilepsy. We have described a homozygous, gain of function substitution mutation (R1584P) in the CaV3.2 T-type Ca2+ channel gene (Cacna1h) in the Genetic Absence Epilepsy Rats from Strasbourg (GAERS). The non-epileptic control (NEC) rats, derived from the same original Wistar strains as GAERS but selectively in-breed not to express seizures, are null for the R1584P mutation. To study the effects of this mutation in rats who otherwise have a GAERS or NEC genetic background, we bred congenic GAERS-Cacna1hNEC (GAERS null for R1584P mutation) and congenic NEC-Cacna1hGAERS (NEC homozygous for R1584P mutation) and evaluated the seizure and behavioral phenotype of these strains in comparison to the original GAERS and NEC strains. MethodsTo evaluate seizure expression in the congenic strains, EEG electrodes were implanted in NEC, GAERS, GAERS-Cacna1hNEC without the R1584P mutation, and NEC-Cacna1hGAERS with the R1584P mutation rats. In the first study, continuous EEG recordings were acquired from week 4 (when seizures begin to develop in GAERS) to week 14 of age (when GAERS display hundreds of seizures per day). In the second study, the seizure and behavioral phenotype of GAERS and NEC-Cacna1hGAERS strains were evaluated during young age (6 weeks of age) and adulthood (16 weeks of age) of GAERS, NEC, GAERS-Cacna1hNEC and NEC-Cacna1hGAERS. The Open field test (OFT) and sucrose preference test (SPT) were performed to evaluate anxiety-like and depressive-like behavior, respectively. This was followed by EEG recordings at 18 weeks of age to quantify the seizures, and spike-wave discharge (SWD) cycle frequency. At the end of the study, the whole thalamus was collected for T-type calcium channel mRNA expression analysis. ResultsGAERS had a significantly shorter latency to first seizures and an increased number of seizures per day compared to GAERS-Cacna1hNEC. On the other hand, the presence of the R1584P mutation in the NEC-Cacna1hGAERS was not enough to generate spontaneous seizures in their seizure-resistant background. 6 and 16-week-old GAERS and GAERS-Cacna1hNEC rats showed anxiety-like behavior in the OFT, in contrast to NEC and NEC-Cacna1hGAERS. Results from the SPT showed that the GAERS developed depressive-like in the SPT compared to GAERS-Cacna1hNEC, NEC, and NEC-Cacna1hGAERS. Analysis of the EEG at 18 weeks of age showed that the GAERS had an increased number of seizures per day, increased total seizure duration and a higher cycle frequency of SWD relative to GAERS-Cacna1hNEC. However, the average seizure duration was not significantly different between strains. Quantitative real-time PCR showed that the T-type Ca2+ channel isoform CaV3.2 channel expression was significantly increased in GAERS compared to NEC, GAERS-Cacna1hNEC and NEC-Cacna1hGAERS. The presence of the R1584P mutation increased the total ratio of CaV3.2 + 25/−25 splice variants in GAERS and NEC-Cacna1hGAERS compared to NEC and GAERS-Cacna1hNEC. DiscussionThe data from this study demonstrate that the R1584P mutation in isolation on a seizure-resistant NEC genetic background was insufficient to generate absence seizures, and that a GAERS genetic background can cause seizures even without the mutation. However, the study provides evidence that the R1584P mutation acts as a modulator of seizures development and expression, and depressive-like behavior in the SPT, but not the anxiety phenotype of the GAERS model of absence epilepsy.