EEG Acquisition Research Articles

Artificial Intelligence-Assisted Evaluation of English Teaching Effectiveness in Colleges and Universities

Abstract Education informatization is an important initiative for the country to realize education modernization, which is related to the development of China’s education. The use of a single evaluation of teaching effectiveness obviously cannot meet the assessment needs. Based on the premise of intelligent assistance through a hybrid brain-computer interface, this paper aims to study an evaluation method for teaching effectiveness using artificial intelligence. Firstly, EEG signals are collected through an EEG acquisition device, and then they are preprocessed. Second, the pre-processed signals are subjected to feature extraction, and then the features are classified using a specific pattern recognition method, and finally the classification results are used to assess the effectiveness of English teaching for college students. Based on the evaluation results, the learning system is optimized and improved. The signals collected by EEG devices carry a lot of noise, and in this paper, the processing of the original signals includes a smoothing filter, de-baseline filter, filter filter, and Kalman filter to achieve a higher signal-to-noise ratio. Multivariate evaluation results are provided by the evaluation system that is assisted by artificial intelligence. Compared with other evaluation methods to enrich the evaluation information, based on the above system effectiveness evaluation study, as far as the F1 value is concerned, this paper’s method is the highest, reaching 0.815, which is 0.048 higher than the highest F1 value of the traditional algorithm, and in the other two values, this paper’s method is also higher than other methods. This verifies the rationality and effectiveness of the teaching effectiveness evaluation system based on a hybrid brain-computer interface proposed in this paper.

Comparative subcutaneous and submuscular implantation of an electroencephalography device for long term electroencephalographic monitoring in dogs.

Implantable electroencephalography (EEG) recording devices have been used for ultra-long-term epilepsy monitoring both in clinical and home settings in people. Objective and accurate seizure detection and recording at home could be of great benefit in diagnosis, management and research in canine idiopathic epilepsy (IE). Continuous EEG monitoring would allow accurate detection of seizure patterns, seizure cycles, and seizure frequency. An EEG acquisition system usable in an "out of clinic" setting could improve owner and veterinary compliance for EEG diagnostics and seizure management. Whether a subcutaneous ultra-long term EEG monitoring device designed for humans could be implanted in dogs. Cadaver study with 8 medium to large breed dogs. Comparatively using a subcutaneous and submuscular approach to implant the UNEEG SubQ-Implant in each dog. Positioning was controlled via CT post implantation and cranial measurements were taken. In four of the eight dogs a submuscular implantation without any complications was possible. Complications were close contact to the optic nerve in the first approaches, before the implantation angle was changed and in the smallest dog contact of the implant with the orbital fat body. Cranial measurements of less than 95 mm length proved to be too small for reliable implantation via this approach. The subcutaneous approach showed severe limitations and the implant was prone to dislocation. The UNEEQ SubQ-Implant can be implanted in dogs, via submuscular approach. CT imaging and cranial measurements should be taken prior to implantation.

EEG Source Imaging-Clinical Considerations for EEG Acquisition and Signal Processing for Improved Temporo-Spatial Resolution.

EEG source imaging (ESI) has gained traction in recent years as a useful clinical tool for the noninvasive surgical work-up of patients with drug-resistant focal epilepsy. Despite its proven benefits for the temporo-spatial modeling of spike and seizure sources, ESI remains widely underused in clinical practice. This partly relates to a lack of clarity around an optimal approach to the acquisition and processing of scalp EEG data for the purpose of ESI. Here, we describe some of the practical considerations for the clinical application of ESI. We focus on patient preparation, the impact of electrode number and distribution across the scalp, the benefit of averaging raw data for signal analysis, and the relevance of modeling different phases of the interictal discharge as it evolves from take-off to peak. We emphasize the importance of recording high signal-to-noise ratio data for reliable source analysis. We argue that the accuracy of modeling cortical sources can be improved using higher electrode counts that include an inferior temporal array, by averaging interictal waveforms rather than limiting ESI to single spike analysis, and by careful interrogation of earlier phase components of these waveforms. No amount of postacquisition signal processing or source modeling sophistication, however, can make up for suboptimally recorded scalp EEG data in a poorly prepared patient.

Drowsiness detection of EEG signals using image-based convolutional neural network

With the continuous development of autonomous driving technology, more drivers relax their attention while driving, resulting in a continuous accumulation of the number of vehicle accidents. The detection of driver's attention can be used as a condition for whether to execute autonomous driving, to help to reduce the probability of major accidents. This study compares the performance of image-based and traditional drowsiness detection trained on the same dataset. Total 5 participants were told to control a computer-simulated train with a 12-channel real-time EEG acquisition device on their heads. We transferred their EEG signals into images with the coordinates of every useful electrode as the training and testing data for our Convolutional Neural Network (CNN). Our results indicated that the CNN model trained on image-based EEG signals achieved much higher accuracy (83.49%) compared with SVM model trained on raw EEG signals, providing an effective method for improving driving safety.

An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action.

A novel multimodal experimental setup and dyadic study protocol were designed to investigate the neurophysiological underpinnings of joint action through the synchronous acquisition of EEG, ECG, EMG, respiration and kinematic data from two individuals engaged in ecologic and naturalistic cooperative and competitive joint actions involving face-to-face real-time and real-space coordinated full body movements. Such studies are still missing because of difficulties encountered in recording reliable neurophysiological signals during gross body movements, in synchronizing multiple devices, and in defining suitable study protocols. The multimodal experimental setup includes the synchronous recording of EEG, ECG, EMG, respiration and kinematic signals of both individuals via two EEG amplifiers and a motion capture system that are synchronized via a single-board microcomputer and custom Python scripts. EEG is recorded using new dry sports electrode caps. The novel study protocol is designed to best exploit the multimodal data acquisitions. Table tennis is the dyadic motor task: it allows naturalistic and face-to-face interpersonal interactions, free in-time and in-space full body movement coordination, cooperative and competitive joint actions, and two task difficulty levels to mimic changing external conditions. Recording conditions-including minimum table tennis rally duration, sampling rate of kinematic data, total duration of neurophysiological recordings-were defined according to the requirements of a multilevel analytical approach including a neural level (hyperbrain functional connectivity, Graph Theoretical measures and Microstate analysis), a cognitive-behavioral level (integrated analysis of neural and kinematic data), and a social level (extending Network Physiology to neurophysiological data recorded from two interacting individuals). Four practical tests for table tennis skills were defined to select the study population, permitting to skill-match the dyad members and to form two groups of higher and lower skilled dyads to explore the influence of skill level on joint action performance. Psychometric instruments are included to assess personality traits and support interpretation of results. Studying joint action with our proposed protocol can advance the understanding of the neurophysiological mechanisms sustaining daily life joint actions and could help defining systems to predict cooperative or competitive behaviors before being overtly expressed, particularly useful in real-life contexts where social behavior is a main feature.

MLDA: Multi-Loss Domain Adaptor for Cross-Session and Cross-Emotion EEG-Based Individual Identification.

Traditional individual identification methods, such as face and fingerprint recognition, carry the risk of personal information leakage. The uniqueness and privacy of electroencephalograms (EEG) and the popularization of EEG acquisition devices have intensified research on EEG-based individual identification in recent years. However, most existing work uses EEG signals from a single session or emotion, ignoring large differences between domains. As EEG signals do not satisfy the traditional deep learning assumption that training and test sets are independently and identically distributed, it is difficult for trained models to maintain good classification performance for new sessions or new emotions. In this article, an individual identification method, called Multi-Loss Domain Adaptor (MLDA), is proposed to deal with the differences between marginal and conditional distributions elicited by different domains. The proposed method consists of four parts: a) Feature extractor, which uses deep neural networks to extract deep features from EEG data; b) Label predictor, which uses full-layer networks to predict subject labels; c) Marginal distribution adaptation, which uses maximum mean discrepancy (MMD) to reduce marginal distribution differences; d) Associative domain adaptation, which adapts to conditional distribution differences. Using the MLDA method, the cross-session and cross-emotion EEG-based individual identification problem is addressed by reducing the influence of time and emotion. Experimental results confirmed that the method outperforms other state-of-the-art approaches.

Self-Supervised EEG Representation Learning with Contrastive Predictive Coding for Post-Stroke Patients.

Stroke patients are prone to fatigue during the EEG acquisition procedure, and experiments have high requirements on cognition and physical limitations of subjects. Therefore, how to learn effective feature representation is very important. Deep learning networks have been widely used in motor imagery (MI) based brain-computer interface (BCI). This paper proposes a contrast predictive coding (CPC) framework based on the modified s-transform (MST) to generate MST-CPC feature representations. MST is used to acquire the temporal-frequency feature to improve the decoding performance for MI task recognition. EEG2Image is used to convert multi-channel one-dimensional EEG into two-dimensional EEG topography. High-level feature representations are generated by CPC which consists of an encoder and autoregressive model. Finally, the effectiveness of generated features is verified by the k-means clustering algorithm. It can be found that our model generates features with high efficiency and a good clustering effect. After classification performance evaluation, the average classification accuracy of MI tasks is 89% based on 40 subjects. The proposed method can obtain effective feature representations and improve the performance of MI-BCI systems. By comparing several self-supervised methods on the public dataset, it can be concluded that the MST-CPC model has the highest average accuracy. This is a breakthrough in the combination of self-supervised learning and image processing of EEG signals. It is helpful to provide effective rehabilitation training for stroke patients to promote motor function recovery.

Mental fatigue assessment by an arbitrary channel EEG based on morphological features and LSTM-CNN

In order to achieve more sensitive mental fatigue assessment (MFA) based on an arbitrary channel EEG, this study proposed a series of feature extraction methods that combine mathematical morphology (MM), as well as an LSTM-CNN architecture. Firstly, 37 subjects had their resting-state EEGs collected at rested wakefulness (RW) and after 24 h of sleep deprivation (SD) using a 30-channel EEG acquisition device, the RW and SD groups were regarded as the negative and positive groups of mental fatigue, respectively, and the EEG collection were further categorized into two conditions: eye-opened state (EO) and eye-closed state (EC). Then, since MM can reflect the morphological characteristics of EEG rhythms and their potentials relatively independently of the time-frequency analysis and phase calculation, the MM methods were found to better reflect the mental fatigue after SD statistically, whether for single features (ANOVA: p<0.000001), multiple features (clustering by K-means, t-test: p<0.01), or time series feature spaces (calculating CD, t-test: p<0.01) of a single channel. Finally, the LSTM-CNN enhanced the generalization ability when dealing with different single-channel EEG by combining GRUs with convolutional layers: comparing the AUCs of different architectures for MFA based on an arbitrary channel, LSTM-CNN (0.992) > LSTM network (0.94) > CNN (0.831) > MLP (0.754). Moreover, the use of MM also improved the accuracy of analyzed architectures, and the true/false positive rate (TPR/FPR) of the LSTM-CNN architecture for MFA based on an arbitrary channel reached 97.024 %/3.497 %, which provided a feasible solution for the arbitrary channel EEG-based MFA.

Time and frequency domain pre-processing for epileptic seizure classification of epileptic EEG signals

Although epilepsy is one of the most prevalent and ancient neurological disorder, but, still difficult to identify the specific type of seizure, due to artefacts, noise, and other disturbances, because of acquisition of Scalp EEG. It necessitating the use of skilled medical professionals as incorrect diagnosis lead to wrong Anti Seizure Drug (ASDs) and face it’s side effects. On the other hand machine learning plays a crucial role in seizure detection by analyzing and identifying patterns in brain activity data that are indicative of seizures. It can be used to develop predictive models that can detect the onset of seizures in real-time, allowing for early intervention and improved patient outcomes. Most of the research work focuses on seizure detection using various machine learning techniques pre-processed by different mathematical models. But, very less attention is paid towards seizure type detection. In this study, multiple Machine and Deep Learning algorithms were used in conjunction with time-domain and frequency-domain pre-processing to classify epileptic seizures into multiple types. The ictal period of various seizure types were extracted from Temple University Hospital EEG (TUHEEG) and the pre-processed data was tried out with multiple classifiers, including support vector classifiers (SVC), K- Nearest Neighbor (KNN), and Long short term memory (LSTM), among others. By using SVM, KNN, and LSTM, multiclass classification of seven types of epileptic seizures with 19 channels were considered for each EEG data and a 75–25 train–test ratio was accomplished with 90.41%, 94.46%, and 86.2% accuracy respectively. Epileptic seizure’s ictal phase EEG signals are categorized using a variety of machine learning(ML) and deep learning(DL) methods after being pre-processed using time domain and frequency domain approaches. The KNN yields the best results of all.

CE Workshop 10: Advances in Event-Related Potential Methods for Assessing Clinical Populations

Abstract &amp; Learning Objectives:Event-related potentials (ERPs) have been used to examine perceptual, cognitive, motor, and affective processes for over 50 years. Although newer techniques provide greater neuroanatomical specificity, the excellent temporal resolution and ease of acquisition of ERPs continue to be highly valuable. In addition, continued methodological refinements have made it possible to answer progressively more sophisticated questions using ERPs. In this presentation, I will describe several key methodological improvements that are now in widespread use or are on the verge of becoming widely used. This will include improved recording methods, such as innovations in electrodes that allow both fast and low-noise EEG acquisition. It will also include EEG preprocessing methods that minimize artifacts and increase the signal-to-noise ratio of the ERPs. Finally, it will include multivariate pattern analysis methods that can be used to "decode" what a participant perceives and stores in working memory. Together, these new methods have dramatically increased the information that can be obtained about dysfunctional brain activity in clinical populations.Upon conclusion of this course, learners will be able to: 1.Evaluate and critically assess ERP studies of clinical populations that were published over the last decade, making use of advanced recording and analysis methods that are now widely used2.Evaluate and critically assess ERP studies of clinical populations that have been published recently or will be published in the near future that take advantage of state-of-the-art recording and analysis methods, such as dry electrodes and multivariate pattern analysis3.Integrate recent and emerging research findings into your research or clinical practice

Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features.

The electroencephalographic (EEG) based on the motor imagery task is derived from the physiological electrical signal caused by the autonomous activity of the brain. Its weak potential difference changes make it easy to be overwhelmed by noise, and the EEG acquisition method has a natural limitation of low spatial resolution. These have brought significant obstacles to high-precision recognition, especially the recognition of the motion intention of the same upper limb. This research proposes a method that combines signal traceability and Riemannian geometric features to identify six motor intentions of the same upper limb, including grasping/holding of the palm, flexion/extension of the elbow, and abduction/adduction of the shoulder. First, the EEG data of electrodes irrelevant to the task were screened out by low-resolution brain electromagnetic tomography. Subsequently, tangential spatial features are extracted by the Riemannian geometry framework in the covariance matrix estimated from the reconstructed EEG signals. The learned Riemannian geometric features are used for pattern recognition by a support vector machine with a linear kernel function. The average accuracy of the six classifications on the data set of 15 participants is 22.47%, the accuracy is 19.34% without signal traceability, the accuracy is 18.07% when the features are the filter bank common spatial pattern (FBCSP), and the accuracy is 16.7% without signal traceability and characterized by FBCSP. The results show that the proposed method can significantly improve the accuracy of intent recognition. In addressing the issue of temporal variability in EEG data for active Brain-Machine Interfaces, our method achieved an average standard deviation of 2.98 through model transfer on different days' data.

Application and Development of EEG Acquisition and Feedback Technology: A Review.

This review focuses on electroencephalogram (EEG) acquisition and feedback technology and its core elements, including the composition and principles of the acquisition devices, a wide range of applications, and commonly used EEG signal classification algorithms. First, we describe the construction of EEG acquisition and feedback devices encompassing EEG electrodes, signal processing, and control and feedback systems, which collaborate to measure faint EEG signals from the scalp, convert them into interpretable data, and accomplish practical applications using control feedback systems. Subsequently, we examine the diverse applications of EEG acquisition and feedback across various domains. In the medical field, EEG signals are employed for epilepsy diagnosis, brain injury monitoring, and sleep disorder research. EEG acquisition has revealed associations between brain functionality, cognition, and emotions, providing essential insights for psychologists and neuroscientists. Brain-computer interface technology utilizes EEG signals for human-computer interaction, driving innovation in the medical, engineering, and rehabilitation domains. Finally, we introduce commonly used EEG signal classification algorithms. These classification tasks can identify different cognitive states, emotional states, brain disorders, and brain-computer interface control and promote further development and application of EEG technology. In conclusion, EEG acquisition technology can deepen the understanding of EEG signals while simultaneously promoting developments across multiple domains, such as medicine, science, and engineering.

Hippocampal injections of soluble amyloid-beta oligomers alter electroencephalographic activity during wake and slow-wave sleep in rats

BackgroundSoluble amyloid-beta oligomers (Aβo) begin to accumulate in the human brain one to two decades before a clinical diagnosis of Alzheimer’s disease (AD). The literature supports that soluble Aβo are implicated in synapse and neuronal losses in the brain regions such as the hippocampus. This region importantly contributes to explicit memory, the first type of memory affected in AD. During AD preclinical and prodromal stages, people are also experiencing wake/sleep alterations such as insomnia (e.g., difficulty initiating sleep, decreased sleep duration), excessive daytime sleepiness, and sleep schedule modifications. In addition, changes in electroencephalographic (EEG) activity during wake and sleep have been reported in AD patients and animal models. However, the specific contribution of Aβo to wake/sleep alterations is poorly understood and was investigated in the present study.MethodsChronic hippocampal injections of soluble Aβo were conducted in male rats and combined with EEG recording to determine the progressive impact of Aβ pathology specifically on wake/sleep architecture and EEG activity. Bilateral injections were conducted for 6 consecutive days, and EEG acquisition was done before, during, and after Aβo injections. Immunohistochemistry was used to assess neuron numbers in the hippocampal dentate gyrus (DG).ResultsAβo injections did not affect the time spent in wakefulness, slow wave sleep (SWS), and paradoxical sleep but altered EEG activity during wake and SWS. More precisely, Aβo increased slow-wave activity (SWA; 0.5–5 Hz) and low-beta activity (16–20 Hz) during wake and decreased theta (5–9 Hz) and alpha (9–12 Hz) activities during SWS. Moreover, the theta activity/SWA ratio during wake and SWS was decreased by Aβo. These effects were significant only after 6 days of Aβo injections and were found with alterations in neuron counts in the DG.ConclusionsWe found multiple modifications of the wake and SWS EEG following Aβo delivery to the hippocampus. These findings expose a specific EEG signature of Aβ pathology and can serve the development of non-invasive and cost-effective markers for the early diagnosis of AD or other amyloid-related diseases.

EEG-based emotion recognition for road accidents in a simulated driving environment

Encountering unexpected events with different levels of danger can cause different levels of emotional changes in the driver, and identifying the driver’s mental state can assist in determining whether he or she can control the vehicle. Electroencephalogram (EEG) is an essential component of the basis for emotional classification. Current driving-related EEG research has focused chiefly on fatigue driving and road rage. In order to explore the connection between panic emotion and accident-avoidance ability, this paper proposes an EEG acquisition and emotion classification scheme in a simulated driving environment. The scheme uses vehicle speed as a variable to simulate obstacle avoidance at different danger levels. It uses graph neural networks (GNN) with functional connectivity and attention mechanisms to simulate the physiological structure of the brain to process the data. In addition, various experiments were conducted to compare the features from entropy and power perspectives. The three-class classification result reached 75.26%, with a single label’s highest F1 score of 76.7%. The binary classification result reached 91.5%, with a single label’s highest F1 score of 91.86%. The experimental results show that the solution can effectively simulate different dangerous situations, capture the driver’s EEG signals, and effectively monitor the emotional state in combination with deep learning models.

Cognitive Impairment in MRI-Negative Epilepsy: Relationship between Neurophysiological and Neuropsychological Measures.

Epileptic patients frequently encounter cognitive impairment. Functions that are mostly affected involve memory, attention, and executive function; however, this is mainly dependent on the location of the epileptic activity. The aim of the present study is to assess cognitive functions in MRI-negative epilepsy patients by means of neurophysiological and neuropsychological measures, as well as study the concept of transient cognitive impairment in patients with epileptiform discharges during EEG acquisition. The patients were enrolled from an outpatient Epilepsy/Clinical Neurophysiology clinic over a time period of 6 months. The study sample comprised 20 MRI-negative epilepsy patients (mean age ± standard deviation (SD), 30.3 ± 12.56 years; age range, 16-60 years; average disease duration, 13.95 years) and 10 age-matched controls (mean age ± SD, 24.22 ± 15.39 years), who were also education-matched (p > 0.05). Patients with epileptogenic lesions were excluded from the study. Informed consent was obtained from all subjects involved in the study. Auditory ERPs and the cognitive screening tool EpiTrack were administered to all subjects. Latencies of P300 and slow waves were prolonged in patients compared to controls (p < 0.05). The ASM load and patients' performance in the EpiTrack maze subtest were the most significant predictors of P300 latency. A decline in the memory, attention, and speed of information processing was observed in patients with cryptogenic epilepsy compared to age-matched controls, as reflected by P300 latency and EpiTrack scores.

Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission.

Brain-computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large. Thus, recording electroencephalography (EEG) signals is an inconvenient task. In recent years, system-on-chip (SoC) approaches have been integrated into BCI research, and sensors for wireless portable devices have been developed; however, there is still considerable work to be done. As neuroscience research has advanced, EEG signal analyses have come to require more accurate data. Due to the limited bandwidth of Bluetooth wireless transmission technology, EEG measurement systems with more than 16 channels must be used to reduce the sampling rate and prevent data loss. Therefore, the goal of this study was to develop a multichannel, high-resolution (24-bit), high-sampling-rate EEG BCI device that transmits signals via Wi-Fi. We believe that this system can be used in neuroscience research. The EEG acquisition system proposed in this work is based on a Cortex-M4 microcontroller with a Wi-Fi subsystem, providing a multichannel design and improved signal quality. This system is compatible with wet sensors, Ag/AgCl electrodes, and dry sensors. A LabVIEW-based user interface receives EEG data via Wi-Fi transmission and saves the raw data for offline analysis. In previous cognitive experiments, event tags have been communicated using Recommended Standard 232 (RS-232). The developed system was validated through event-related potential (ERP) and steady-state visually evoked potential (SSVEP) experiments. Our experimental results demonstrate that this system is suitable for recording EEG measurements and has potential in practical applications. The advantages of the developed system include its high sampling rate and high amplification. Additionally, in the future, Internet of Things (IoT) technology can be integrated into the system for remote real-time analysis or edge computing.

Research on Eye Movement Classification Based on the Deep Learning Model

To improve the recognition accuracy of eye movement direction based on EOGM, the Emotiv Epoc, and EEG acquisition headset was used to collect the EEG signals of the subjects in real time. After the 14-channel signals were filtered by wavelet, the short-time energy calculation method was used to select the two channels most sensitive to eye movement. The long and short-term memory network (LSTM) was then used to classify the eye movement signals. On the same data set, the experimental results show that the classification rates in the upper, lower, left, and right directions are up to 91%, 90%, 83%, and 75%, respectively, and the average classification rate is up to 84%. Compared with the existing classification methods, the classification accuracy of the proposed method is higher and the implementation process of the classification algorithm is simpler, which further verifies the feasibility and effectiveness of using EEG to identify the direction of eye movement.

Crowdsourced EEG experiments: A proof of concept for remote EEG acquisition using EmotivPRO Builder and EmotivLABS

The development of online research platforms has made data collection more efficient and representative of populations. However, these benefits have not been available for use with cognitive neuroscience tools such as electroencephalography (EEG). In this study, we introduce an approach for remote EEG data collection. We demonstrate how an experiment can be built via the EmotivPRO Builder and deployed to the EmotivLABS website where it can be completed by participants who own EMOTIV EEG headsets. To demonstrate the data collection technique, we collected EEG while participants engaged in a resting state task where participants sat with their eyes open and then eyes closed for 2 min each. We observed a significant difference in alpha power between the two conditions thereby demonstrating the well-known alpha suppression effect. Thus, we demonstrate that EEG data collection, particularly for frequency domain analysis, can be successfully conducted online.

Point-of-care electroencephalography enables rapid evaluation and management of non-convulsive seizures and status epilepticus in the emergency department.

To describe our institutional experience with point-of-care electroencephalography (pocEEG) and its impact on the evaluation/management of suspected non-convulsive seizures in the emergency department (ED). We retrospectively identified 157 adults who underwent pocEEG monitoring in our community hospital ED in 1 year. We calculated the time to obtain pocEEG in the ED (door-to-EEG time) and examined the impact of pocEEG findings (categorized as seizure, highly epileptiform patterns, slowing, or normal activity) on antiseizure medication treatment. PocEEG revealed seizures (14%, n=22), highly epileptiform patterns (22%, n=34), slowing (44%, n=69), and normal activity (20%, n=32). The median door-to-EEG time (from initial ED evaluation to pocEEG monitoring) was only 1.2hours (interquartile range 0.1-2.1) even though 55% of studies were performed after-hours (5 pm-9 am). Most patients were admitted (54% to the intensive care unit, 41% to floor). Antiseizure medication treatment occurred pre-pocEEG in 93 patients (59%) and post-pocEEG in 88 patients (56%). By reviewing the relationship between pocEEG monitoring and antiseizure medication management, we found a significant association between pocEEG findings and changes in management(P<0.001). Treatment escalation occurred more frequently in patients with epileptiform activity (seizures or highly epileptiform patterns, 52%) than patients with non-epileptiform activity (normal or slow, 25%, P<0.001), and avoidance of treatment escalation occurred more frequently in patients with normal or slow activity (27%) than patients with seizures or highly epileptiform patterns (2%, P<0.001). Our study, the largest to date describing the real-world use of pocEEG in emergency medicine, found that rapid EEG acquisition in the ED was feasible in a community hospital and significantly affected the management of suspected non-convulsive seizures.

Disease-modifying effects of a glial-targeted inducible nitric oxide synthase inhibitor (1400W) in mixed-sex cohorts of a rat soman (GD) model of epilepsy

BackgroundAcute exposure to seizurogenic organophosphate (OP) nerve agents (OPNA) such as diisopropylfluorophosphate (DFP) or soman (GD), at high concentrations, induce immediate status epilepticus (SE), reactive gliosis, neurodegeneration, and epileptogenesis as a consequence. Medical countermeasures (MCMs—atropine, oximes, benzodiazepines), if administered in < 20 min of OPNA exposure, can control acute symptoms and mortality. However, MCMs alone are inadequate to prevent OPNA-induced brain injury and behavioral dysfunction in survivors. We have previously shown that OPNA exposure-induced SE increases the production of inducible nitric oxide synthase (iNOS) in glial cells in both short- and long- terms. Treating with a water soluble and highly selective iNOS inhibitor, 1400W, for 3 days significantly reduced OPNA-induced brain changes in those animals that had mild–moderate SE in the rat DFP model. However, such mitigating effects and the mechanisms of 1400W are unknown in a highly volatile nerve agent GD exposure.MethodsMixed-sex cohort of adult Sprague Dawley rats were exposed to GD (132 μg/kg, s.c.) and immediately treated with atropine (2 mg/kg, i.m) and HI-6 (125 mg/kg, i.m.). Severity of seizures were quantified for an hour and treated with midazolam (3 mg/kg, i.m.). An hour post-midazolam, 1400W (20 mg/kg, i.m.) or vehicle was administered daily for 2 weeks. After behavioral testing and EEG acquisition, animals were euthanized at 3.5 months post-GD. Brains were processed for neuroinflammatory and neurodegeneration markers. Serum and CSF were used for nitrooxidative and proinflammatory cytokines assays.ResultsWe demonstrate a significant long-term (3.5 months post-soman) disease-modifying effect of 1400W in animals that had severe SE for > 20 min of continuous convulsive seizures. 1400W significantly reduced GD-induced motor and cognitive dysfunction; nitrooxidative stress (nitrite, ROS; increased GSH: GSSG); proinflammatory cytokines in the serum and some in the cerebrospinal fluid (CSF); epileptiform spikes and spontaneously recurring seizures (SRS) in males; reactive gliosis (GFAP + C3 and IBA1 + CD68-positive glia) as a measure of neuroinflammation, and neurodegeneration (especially parvalbumin-positive neurons) in some brain regions.ConclusionThese findings demonstrate the long-term disease-modifying effects of a glial-targeted iNOS inhibitor, 1400W, in a rat GD model by modulating reactive gliosis, neurodegeneration (parvalbumin-positive neurons), and neuronal hyperexcitability.