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Epilepsy is a severe neurological disease with high prevalence and morbidity worldwide. The unpredictability of seizures prevents physicians from tailoring drugs and therapies. Recent non-invasive seizure prediction research has not improved the overall quality of life for patients. Therefore, new research studies on seizure prediction must integrate data, embedded devices, and algorithms. For a seizure prediction system to emerge as a feasible solution, we must address a reduction in EEG scalp electrode channels, along with a decrease in computational resources to train the time-series signal. In this work, we propose an optimized patient-specific channel reduction for seizure prediction using Model Agnostic Meta-Learning (MAML) applied to a Deep Neural Network (DNN). We selected and optimized the number of channels from each of the 23 subjects of the CHB-MIT Dataset. The feature vectors are extracted using Ensemble Empirical Mode Decomposition (EEMD) and Sequential Feature Selection (SFS). We implemented the MAML model to classify the small EEG data generated from the reduced number of subject-dependent electrodes. The experiment results yield an average sensitivity and specificity of 91% and 90%, respectively. Our study demonstrates that MAML is a promising approach to learn EEG patterns to predict epileptic seizures with few EEG scalp electrodes.

ObjectiveTo investigate the diagnostic utility of high frequency oscillations (HFOs) via scalp electroencephalogram (EEG) in infantile spasms.MethodsWe retrospectively analyzed interictal slow-wave sleep EEGs sampled at 2,000Hz recorded from 30 consecutive patients who were suspected of having infantile spasms. We measured the rate of HFOs (80-500Hz) and the strength of the cross-frequency coupling between HFOs and slow-wave activity (SWA) at 3-4Hz and 0.5-1Hz as quantified with modulation indices (MIs).ResultsTwenty-three patients (77%) exhibited active spasms during the overnight EEG recording. Although the HFOs were detected in all children, increased HFO rate and MIs correlated with the presence of active spasms (p&nbsp

Technological advances in the semiconductor industry and the increasing demand and development of wearable medical systems have enabled the development of dedicated chips for complex electroencephalogram (EEG) signal processing with smart functions and artificial intelligence‐based detections/classifications. Around 10 million transistors are integrated into a 1 mm2 silicon wafer surface in the dedicated chip, making wearable EEG systems a powerful dedicated processor instead of a wireless raw data transceiver. The reduction of amplifiers and analog‐digital converters on the silicon surface makes it possible to place the analog front‐end circuits within a tiny packaged chip; therefore, enabling high‐count EEG acquisition channels. This article introduces and reviews the state‐of‐the‐art dedicated chip designs for EEG processing, particularly for wearable systems. Furthermore, the analog circuits and digital platforms are included, and the technical details of circuit topology and logic architecture are presented in detail.

ObjectiveTo examine the relationship between scalp EEG biomarkers of hyperexcitability in Alzheimer disease (AD) and to determine how these electric biomarkers relate to the clinical expression of seizures in AD.MethodsIn this cross-sectional study, we performed 24-hour ambulatory scalp EEGs on 43 cognitively normal elderly healthy controls (HC), 41 participants with early-stage AD with no history or risk factors for epilepsy (AD-NoEp), and 15 participants with early-stage AD with late-onset epilepsy related to AD (AD-Ep). Two epileptologists blinded to diagnosis visually reviewed all EEGs and annotated all potential epileptiform abnormalities. A panel of 9 epileptologists blinded to diagnosis was then surveyed to generate a consensus interpretation of epileptiform abnormalities in each EEG.ResultsEpileptiform abnormalities were seen in 53% of AD-Ep, 22% of AD-NoEp, and 4.7% of HC. Specific features of epileptiform discharges, including high frequency, robust morphology, right temporal location, and occurrence during wakefulness and REM, were associated with clinical seizures in AD. Multiple EEG biomarkers concordantly demonstrated a pattern of left temporal lobe hyperexcitability in early stages of AD, whereas clinical seizures in AD were often associated with bitemporal hyperexcitability. Frequent small sharp spikes were specifically associated with epileptiform EEGs and thus identified as a potential biomarker of hyperexcitability in AD.ConclusionEpileptiform abnormalities are common in AD but not all equivalent. Specific features of epileptiform discharges are associated with clinical seizures in AD. Given the difficulty recognizing clinical seizures in AD, these EEG features could provide guidance on which patients with AD are at high risk for clinical seizures.

The scalp electroencephalogram (EEG), a non-invasive measure of brain's electrical activity, is commonly used ancillary test to aide in the diagnosis of epilepsy. Usually, neurologists employ direct visual inspection to identify epileptiform abnormalities. Therefore, electroencephalograms have been an essential integral to the researches which aim to automatically detect epilepsy. However, it is difficult because seizure manifestations on scalp EEG are extremely variable between patients, even the same patient. In addition, scalp EEG is usually composed of large number of noise signals which might cover the real features of seizure. To this challenge, we construct an 18-layer Long-Term recurrent convolutional network (LRCN) to automatic epileptogenic zone recognition and localization on scalp EEG. As far as we know, we are the first to train a deep learning classifier to identify seizures through the EEG images, just like neurologists direct visual inspection to identify epileptiform abnormalities. Furthermore, unlike the traditionally methods extracted features from channels manually, which neglected the association of brain's epileptiform abnormalities electrical transmission, seizures is considered as a continuous brain's abnormal electrical activity in our algorithm, from produce at one or several channels, transmission between channels, to flat again after seizures. The method was evaluated in 23 patients with a total of 198 seizures. The classifier shows reasonably good results, with 84% for sensitivity, 99% for specificity, and 99% for accuracy. False Positive Rate per hours exceeds significantly previous results obtained on cross-patient classifiers, with 0.2/h.

Our aim was to demonstrate the first use of Optically Pumped Magnetoencephalography (OP-MEG) in an epilepsy patient with unrestricted head movement. Current clinical MEG uses a traditional SQUID system for recording MEG signal, where sensors are cryogenically cooled and housed in a helmet in which the patient’s head is fixed. Here we use a different type of sensor (OPM), which operates at room temperature and can be placed directly on the patient’s scalp, permitting free head movement. We performed two 30 minute OP-MEG recording sessions in a patient with refractory focal epilepsy and compared these with clinical scalp EEG performed earlier. OP-MEG was able to identify analogous interictal activity to scalp EEG, and source localise this activity to an appropriate brain region. This is the first application of OP-MEG in human epilepsy. Future directions include simultaneous EEG/OP-MEG recording and prolonged OP-MEG telemetry.

Herein, the authors report for the first time, scalp-recorded (1) focal ictal direct current (DC) shifts (active DC shifts; that precede conventional pattern) from the chronic focus of focal epilepsy and (2) ictal high-frequency oscillation after ictal DC shifts (passive DC shifts; that follow both conventional and high-frequency oscillation ictal patterns) from the acute focus of acute symptomatic seizures (Szs) in a 77-year-old man. Sixteen episodes of clinical Szs were recorded by scalp EEG with a 2-seconds time constant. Among the 16 recorded episodes of Sz, four EEG Sz patterns originated from the left posterior temporal area (chronic focus), and all patterns (100%) exhibited active DC shifts preceding the conventional pattern by 12 seconds. Twelve EEG Sz patterns originated from the right parietal area (acute focus), and the high-frequency oscillations (five Szs) (41.6%) and DC shifts (six Szs) (50%) occurred first, followed by the conventional pattern 8 seconds later. Because both the active and the passive DC shifts were recorded with a time constant of 2 seconds, which was smaller than that reported previously for ictal DC shifts (e.g., time constant of 10 seconds), clinically useful ictal DC shifts could be routinely inspected with a time constant of 2 seconds.

Gamma-band oscillations (GBOs) elicited by transient nociceptive stimuli are one of the most promising biomarkers of pain across species. Still, whether these GBOs reflect stimulus encoding in the primary somatosensory cortex (S1) or nocifensive behavior in the primary motor cortex (M1) is debated. Here we recorded neural activity simultaneously from the brain surface as well as at different depths of the bilateral S1/M1 in freely-moving male rats receiving nociceptive stimulation., Gamma-band oscillations (GBOs) elicited by transient nociceptive stimuli are one of the most promising biomarkers of pain across species. Still, whether these GBOs reflect stimulus encoding in the primary somatosensory cortex (S1) or nocifensive behavior in the primary motor cortex (M1) is debated. Here we recorded neural activity simultaneously from the brain surface as well as at different depths of the bilateral S1/M1 in freely-moving male rats receiving nociceptive stimulation. GBOs measured from superficial layers of S1 contralateral to the stimulated paw not only had the largest magnitude, but also showed the strongest temporal and phase coupling with epidural GBOs. Also, spiking of superficial S1 interneurons had the strongest phase coherence with epidural GBOs. These results provide the first direct demonstration that scalp GBOs, one of the most promising pain biomarkers, reflect neural activity strongly coupled with the fast spiking of interneurons in the superficial layers of the S1 contralateral to the stimulated side. SIGNIFICANCE STATEMENT Nociceptive-induced gamma-band oscillations (GBOs) measured at population level are one of the most promising biomarkers of pain perception. Our results provide the direct demonstration that these GBOs reflect neural activity coupled with the spike firing of interneurons in the superficial layers of the primary somatosensory cortex (S1) contralateral to the side of nociceptive stimulation. These results address the ongoing debate about whether nociceptive-induced GBOs recorded with scalp EEG or epidurally reflect stimulus encoding in the S1 or nocifensive behavior in the primary motor cortex (M1), and will therefore influence how experiments in pain neuroscience will be designed and interpreted.

Aim: to evaluate the patients who had epilepsy surgery and pathologically proven focal cortical dysplasia (FCD) in order to further classify and discuss electroencephalography (EEG) findings in different pathological subtypes. Material and methods: this study included 19 refractory epilepsy patients who underwent surgery between 1999 and 2017 in the Istanbul Faculty of Medicine. Demographic data, preoperative examinations, scalp video EEGs, and postoperative outcomes were evaluated retrospectively. Results: In this study, 36.8% of the patients were female. The mean age was 21.89 ± 14.64 years. Rhythmic epileptiform discharges (RED) were observed in 31.6%. 37.5% of the patients with isolated intermittent spike/sharp waves were type I, 50% were type II, and 12.5% were type III. 100% of the patients with normal background activity were FCD type II. 67% of the patients with asymmetric slowing were FCD type I, 22% was FCD type II, 11% were FCD type III. 71% of the patients with symmetrical slowing were FCD type I, 29% were FCD type II. One patient had Frontal Intermittent Rhythmic Activity, one patient had Electrical Status Epilepticus in Slow Sleep, two patients had “burst suppression,” and one patient had a “switch of” sign. The frequency of focal epileptogenic activity was higher when there was an FCD lesion on magnetic resonance imaging. Conclusion: the findings obtained in this study did not reveal any distinctive electrophysiological features in FCD and subgroups of FCD. The incidence of REDs did not differ between types. The frequency of isolated intermittent sharp/spike waves was higher in type II than I. Intermittent and continuous EEG slowing was more commonly seen among FCD Type I patients., NA

In people with drug resistant epilepsy (DRE), seizures are unpredictable, often occurring with little or no warning. The unpredictability causes anxiety and much of the morbidity and mortality of seizures. In this work, 102 seizures of mesial temporal lobe onset were analyzed from 19 patients with DRE who had simultaneous intracranial EEG (iEEG) and scalp EEG as part of their surgical evaluation. The first aim of this paper was to develop machine learning models for seizure prediction and detection (i) using iEEG only, (ii) scalp EEG only and (iii) jointly analyzing both iEEG and scalp EEG. The second goal was to test if machine learning could detect a seizure on scalp EEG when that seizure was not detectable by the human eye (surface negative) but was seen in iEEG. The final question was to determine if the deep learning algorithm could correctly lateralize the seizure onset. The seizure detection and prediction problems were addressed jointly by training Deep Neural Networks (DNN) on 4 classes: non-seizure, pre-seizure, left mesial temporal onset seizure and right mesial temporal onset seizure. To address these aims, the classification accuracy was tested using two deep neural networks (DNN) against 3 different types of similarity graphs which used different time series of EEG data. The convolutional neural network (CNN) with the Waxman similarity graph yielded the highest accuracy across all EEG data (iEEG, scalp EEG and combined). Specifically, 1 second epochs of EEG were correctly assigned to their seizure, pre-seizure, or non-seizure category over 98% of the time. Importantly, the pre-seizure state was classified correctly in the vast majority of epochs (>97%). Detection from scalp EEG data alone of surface negative seizures and the seizures with the delayed scalp onset (the surface negative portion) was over 97%. In addition, the model accurately lateralized all of the seizures from scalp data, including the surface negative seizures. This work suggests that highly accurate seizure prediction and detection is feasible using either intracranial or scalp EEG data. Furthermore, surface negative seizures can be accurately predicted, detected and lateralized with machine learning even when they are not visible to the human eye.

Theta oscillations (∼4–12 Hz) are dynamically modulated by speed and direction in freely moving animals. However, due to the paucity of electrophysiological recordings of freely moving humans, this mechanism remains poorly understood. Here, we combined mobile-EEG with fully immersive virtual-reality to investigate theta dynamics in twenty-two healthy adults (aged 18–29 years old) freely navigating a T-maze to find rewards. Our results revealed three dynamic periods of theta modulation: 1) theta power increases coincided with the participants’ decision-making period; 2) theta power increased for fast and leftward trials as subjects approached the goal location; and 3) feedback onset evoked two phase-locked theta bursts over the right temporal and frontal-midline channels. These results suggest that recording scalp EEG in freely moving humans navigating a simple virtual T-maze can be utilized as a powerful translational model by which to map theta dynamics during “real-life” goal-directed behavior in both health and disease.

Optically Pumped Magnetometer based Magnetoencephalography (OP-MEG) has significant potential for clinical use in pre-surgical planning in epilepsy. Unlike current clinical MEG, the sensors do not require cryogenic cooling and so can be placed directly on the patient’s head. This allows the patient to move during the recording and means that the sensor positions can be chosen to suit the patient’s head shape and suspected epileptogenic focus. However, OP-MEG is a new technology, and more work is needed to demonstrate this potential. We present OP-MEG recordings from a patient (male in their 30s) with a radiologically identifiable focal cortical dysplasia (FCD) in the right superior frontal sulcus approximately 1.9 cm3 in volume. Previous scalp EEG studies and prolonged video-EEG telemetry did not identify any interictal epileptiform abnormalities. We recorded 30 minutes of OP-MEG with 62-channel, whole-head sensor coverage. During the experiment, the patient’s head was unconstrained. We localised interictal epileptiform discharges (IEDs) from this recording with a beamformer and by fitting a dipole to the averaged IED data. Both the beamformer peak and dipole fit locations were within 2.3 cm of the MRI lesion boundary. This single subject, proof-of-concept recording provides further evidence that OP-MEG can be a useful and minimally invasive tool in the clinical evaluation of epilepsy.

The sensorimotor beta rhythm (∼13–30 Hz) is commonly seen in relation to movement. It is important to understand its functional/behavioral significance in both health and disease. Sorting out competing theories of sensorimotor beta is hampered by a paucity of experimental protocols in humans that manipulate/induce beta oscillations and test their putative effects on concurrent behavior. Here, we developed a novel behavioral paradigm to generate beta and then test its functional relevance. In two human experiments with scalp EEG (n = 11 and 15), we show that a movement instruction generates a high beta state (postmovement beta rebound), which then slows down subsequent movements required during that state. We also show that this high initial beta rebound related to reduced mu–beta desynchronization for the subsequent movement and, further, that the temporal features of the beta state, that is, the beta bursts, related to the degree of slowing. These results suggest that increased sensorimotor beta in the postmovement period corresponds to an inhibitory state—insofar as it retards subsequent movement. By demonstrating a behavioral method by which people can proactively create a high beta state, our paradigm provides opportunities to test the effect of this state on sensations and affordances. It also suggests related experiments using motor imagery rather than actual movement, and this could later be clinically relevant, for example, in tic disorder.

© 2020 by the American Clinical Neurophysiology Society.Background:Glossokinetic artifact (GKA) is a well-known scalp EEG artifact characterized by deflections within the delta to low-theta frequency bands and dynamic polarity typically attributed to the direction of tongue movement. This study aims to investigate intracranial EEG correlations of scalp-GKA. If the tongue is a dipole, per the conventional view, then volume-conducted deflections are expected in the nearest frontal intracranial EEG contacts.Materials and Methods:Simultaneous scalp and intracranial EEG recordings were evaluated in five consecutive medically resistant epilepsy patients at Yale Epilepsy Center in 2017 and 2018, who had classic GKA deflections on scalp EEG. The EEG was sampled at 2,048 to 4,096 Hz and analyzed visually, using a reference placed in the diploic space or over the convexity, and confirmed quantitatively by a statistical framework. Ten GKA deflections were analyzed per case.Results:The medians of age at the time of recording, contacts per case, and amplitude of scalp GKA deflections were 35 years (range: 20-41 years), 171 contacts (range: 165-241 contacts), and 56 V (range: 51-72 V), respectively. There were no slow discharges in the frontal intracranial EEG contacts synchronized with the scalp GKA, either in the delta (1-3 Hz) or in the sub-delta (0.1-1 Hz) bands. However, the expected physiologic attenuation of alpha and beta rhythms and the emergence of high-gamma activity were observed over the peri-Rolandic regions in the invasive recordings.Conclusions:The traditional view of the tongue as a dipole generator of scalp GKA is simplistic and does not account for the findings reported herein. The tongue most probably shunts other scalp and soft-tissue currents. Knowledge of tongue potentials is of interest in the education and the design of tongue-computer interfaces.

Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder®), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.

During Sustained Attention to stimuli across many modalities neural activity often decreases over time on task, while Errors in task performance increase (Vigilance Decrement). Sustained Attention to pain has rarely been investigated experimentally despite its clinical significance. We have employed a Sustained Attention protocol (Continuous Performance Task, CPT) in which the subject counts painful laser stimuli (targets) when they occur randomly in a prolonged train of nonpainful nontargets. We hypothesize that the magnitude of the poststimulus oscillatory power divided by baseline power (Event-Related Spectral Perturbation, ERSP - scalp EEG) over Frontoparietal structures will decrease at all frequencies with time on task, while Beta ERSP (14-30Hz) will be correlated with Error Rates in performance of the CPT. During the CPT with a painful target ERSP was found in four separate Windows, as defined by both their frequency band and the time after the stimulus. A Vigilance Decrement was found which confirms that Sustained Attention to pain was produced by this CPT. In addition, Error Rates was correlated inversely with laser energy, and with ratings of pain unpleasantness and salience. Error Rates also were related directly to the Beta ERSP Window at scalp EEG electrodes over the central sulcus. Over time on task, the ERSP magnitude decreased in Alpha (8-14Hz) Window, was unchanged in early and late Delta/Theta Windows (0-8Hz), and increased in the Beta Window. The increase in Beta ERSP and a decrease in the Alpha ERSP occurred at the same EEG electrode over the parietal lobe to a significant degree across subjects. Overall, Beta activity increases with time on task, and with higher Error Rates as in the case of other modalities. In the case of pain increased Errors correspond to misidentification of painful and nonpainful stimuli and so modulate the sensation of pain under the influence of Sustained Attention.

Background The diagnosis of nonconvulsive status epilepticus (NCSE) in patients with nonepileptiform EEG patterns remains a challenge. Objective To evaluate the usefulness of single photon emission computerized tomography (SPECT) and its quantification (QtSPECT) in the diagnosis of NCSE. Methods We retrospectively reviewed patients admitted with clinical suspicion of NCSE who underwent an HMPAO-SPECT simultaneously with scalp EEG showing nonepileptiform patterns, in a 5-year period. After a complete diagnostic workup, treatment, and clinical evolution, disregarding the SPECT results, patients were classified into confirmed NCSE (n = 11) and non-NCSE (n = 8). Then, we compared the EEG and SPECT results in both groups. Results Lateralized rhythmic delta activity (LRDA) was predominant in the NCSE group (45.4%, p = .045), while lateralized irregular slowing was observed equally in both groups. Patients with NCSE showed significant hyperperfusion compared with non-NCSE patients (p = .026). QtSPECT correctly classified 91% of patients in NCSE and 75% patients with non-NCSE (p = .006). Conclusions Regional cerebral blood flow measured with SPECT could be useful in the diagnosis of NCSE in cases of an EEG pattern with lateralized slow activity and high clinical suspicion.

Localization of the epileptic focus is crucial for epilepsy surgery. Pre-ictal EEG and interictal epileptic discharges are commonly used to localize the focus. Post-ictal scalp EEG may provide useful information for localizing the epileptic focus. This study proposed a non-invasive procedure to localize the epileptic focus via the EEG source imaging (ESI) and epileptic network analysis. Scalp EEG from two patients with drug-resistant epilepsy were used, and two segments of post-ictal EEG were analyzed. The sLORETA algorithm was applied to obtain signals in source space using the patient specific head model. Then we extracted the representative source signals of each brain area by singular value decomposition (SVD). The epileptic networks of different frequency bands in the source space were constructed by Granger causality analysis. The results showed that the regions identified by in-degree feature of low-frequency post-ictal epileptic network were concordant with surgical resected areas. The preliminary result indicates that post-ictal epileptic network in low frequency may potentially be used to identify the ictal focus for surgical planning.

Objective We aimed to explore the feasibility of using scalp-recorded high-frequency oscillations (HFOs) to evaluate the efficacy and prognosis of adrenocorticotropic hormone (ACTH) treatment in patients with infantile spasms. Methods Thirty-nine children with infantile spasms were enrolled and divided into seizure-free and non-seizure-free groups after ACTH treatment. Patients who were seizure-free were further divided into relapse and non-relapse subgroups based on the observations made during a 6-month follow-up period. Scalp ripples were detected and compared during the interictal periods before and after 2 weeks of treatment. Results After ACTH treatment, the number and channels of ripples were significantly lower, whereas the percentage decrease in the number, spectral power, and channels of ripples was significantly higher in the seizure-free group than in the non-seizure-free group. In addition, the relapse subgroup showed higher number and spectral power and wider distribution of ripples than did the non-relapse subgroup. Changes in HFOs in terms of number, spectral power, and channel of ripples were closely related to the severity of epilepsy and can indicate disease susceptibility. Significance Scalp HFOs can be used as an effective biomarker to monitor the effect and evaluate the prognosis of ACTH therapy in patients with infantile spasms.

Objective: To assess the localizing value of interictal scalp EEG in epileptic focal cortical dysplasia (FCD) patients.Methods: A retrospective observational study of EEG records of epileptic patients seen at the Marmara University Hospital was performed. EEG and MRI findings were evaluated to detect a possible correlation. Results: EEG findings were consistent with MRI findings in 6 patients (37.5%).Conclusion: EEG findings were irrelevant in 62.5% (n:10) of patients. Additional imaging techniques such as ictal SPECT, MEG etc. will clearly augment the success of lesion localisation in FCD patients.

An accurate epileptic seizure prediction algorithm can alleviate the problem and reduce risks in the life of a patient suffering from epilepsy. The main motive of this work is to propose a model which can predict seizures well in advance of its occurrence. Multivariate statistical process control (MSPC) has been used for seizure predictions in long-term scalp EEG signal. It has been observed that excessive neuronal activity in the preictal period of seizure changes the electrical characteristic from chaotic to rhythmic behavior. These changes have been utilized for prediction. Eight temporal based features are used for predicting the seizures by using multivariate statistical process control, which is widely known as an anomaly monitoring method. 90 seizures from the CHB-MIT EEG data of ten patients are analyzed. Result The results of the proposed method demonstrated that 80 seizures out of 90 in preictal period were correctly predicted prior to the seizure onset, thereby giving a sensitivity of 88.89%. The false positive rate is observed to 0.39 per hour. Conclusion This study proposed a temporal based patient-specific epileptic seizure prediction method using MSPC in long-term scalp EEG signals. It also provides the possibility of realizing an EEG-based epileptic seizure prediction system which requires less computational power. Significance The proposed method does not require preictal data for modeling. The extracted features are computationally easy. The tested result shows good accuracy on the CHB-MIT data base.

Epilepsy is one of most common neurological disorders that affects people of all ages and can cause unpredictable seizures which may even cause death. The prediction of epileptic activities thus can have a great impact in avoiding fatal injuries through early preparation with medicines and also in improving the efficacy of medicines. A technique for early prediction of epileptic seizure from EEG signal is proposed in this paper. The pre-ictal period of epileptic seizure clearly depicts a start of seizure and comparing the changes in entropy of EEG signals in different brain regions during the pre-seizure period, the proposed technique could successfully predict the seizure using entropy analysis. Moreover, the region of the epileptic activities was also localized by dividing the total brain into four topographic regions and by calculating the entropy from this four zones separately. Thus, this proposed technique has the potential to help the clinical neurologists to investigate seizure detection and treat the patient in a better way with less supervision and better accuracy. Bangladesh Journal of Medical Physics Vol.11 No.1 2018 P 16-25

Sleep is a key phenomenon to both understanding, diagnosing and treatment of many illnesses, as well as for studying health and well being in general. Today, the only widely accepted method for clinically monitoring sleep is the polysomnography (PSG), which is, however, both expensive to perform and influences the sleep. This has led to investigations into light weight electroencephalography (EEG) alternatives. However, there has been a substantial performance gap between proposed alternatives and PSG. Here we show results from an extensive study of 80 full night recordings of healthy participants wearing both PSG equipment and ear-EEG. We obtain automatic sleep scoring with an accuracy close to that achieved by manual scoring of scalp EEG (the current gold standard), using only ear-EEG as input, attaining an average Cohen’s kappa of 0.73. In addition, this high performance is present for all 20 subjects. Finally, 19/20 subjects found that the ear-EEG had little to no negative effect on their sleep, and subjects were generally able to apply the equipment without supervision. This finding marks a turning point on the road to clinical long term sleep monitoring: the question should no longer be whether ear-EEG could ever be used for clinical home sleep monitoring, but rather when it will be.

Objective We studied ripple-band (80–200 Hz) high-frequency oscillations in scalp electroencephalogram (EEG) in various pediatric epilepsies featuring sleep-activated spikes, such as epileptic encephalopathy with continuous spike-and-wave during sleep (CSWS) and investigated their characteristics. Methods The subjects were 94 children with epileptic disorders including idiopathic and non-idiopathic CSWS, benign epilepsy with centrotemporal spikes (BECTS), Panayiotopoulos syndrome, other types of focal epilepsies (oFE), and focal spikes without clinical seizures (Latent). We detected ripple oscillations using a semi-automatic detection tool based on localized power increase. Results In the idiopathic CSWS Group, the median ratio of ripples per spike in the initial EEG was 5.73, which was significantly higher than those in the BECTS, Panayiotopoulos syndrome, oFE, and Latent Groups (0.39, 0.02, 0.35, 0, respectively, all with p Conclusions This paper is the first to confirm a high ratio of ripples per spike in CSWS in the largest number of patients to date. Significance The dense generation of ripples, which occurs through a combination of heavy loading of individual spikes with ripples and large numbers of spikes during sleep, characterizes CSWS and might be closely related to the pathophysiology of this epileptic encephalopathy.

Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medication or surgery does not effectively control seizure activity. A method that predicts the onset of a seizure before it occurs may prove useful as patients might be alerted to make themselves safe or seizures could be prevented with therapeutic interventions just before they occur. Abnormal neuronal activity, the preictal state, starts a few minutes before the onset of a seizure. In recent years, different methods have been proposed to predict the start of the preictal state. These studies follow some common steps, including recording of EEG signals, preprocessing, feature extraction, classification, and postprocessing. However, online prediction of epileptic seizures remains a challenge as all these steps need further refinement to achieve high sensitivity and low false positive rate. In this paper, we present a comparison of state-of-the-art methods used to predict seizures using both scalp and intracranial EEG signals and suggest improvements to existing methods.

Objective To determine the clinical implications of scalp ictal EEG pattern in patients with temporal lobe epilepsy (TLE). Methods Scalp EEG ictal patterns were retrospectively determined in 27 consecutive patients with medically refractory temporal lobe epilepsy who underwent phase-1 scalp video-EEG and phase-2 simultaneous scalp and intracranial video-EEG recordings for pre-surgical evaluation. Results Of the 192 temporal lobe seizures recorded during phase-1 and phase-2 scalp video-EEG studies, 124 (65%) seizures were associated with theta/alpha (5–9 Hz) ictal onset pattern, and 68 (35%) seizures were associated with delta (2–5 Hz) ictal onset pattern. Fourteen (52%) patients had exclusively theta/alpha ictal onset, 3 (11%) patients had exclusively delta ictal onset, and 10 (37%) patients had mixed theta/alpha and delta ictal onsets. MTLE was observed in 26 patients who had 124 seizures with theta/alpha ictal onset and 59 seizures with delta ictal onset. LTLE was observed in one patient who had 9 seizures with delta ictal onset. Scalp ictal EEG pattern was not significantly correlated with postsurgical seizure outcomes. Conclusions Both scalp delta and theta/alpha ictal onset patterns can be commonly found in patients with MTLE. Significance Scalp delta ictal onset is not a unique EEG pattern for LTLE as commonly believed.

Identifying the localization, distribution, and polarity of waveforms are the prime goals of clinical scalp EEG analysis. Appropriate choices of bipolar and referential montages are keys to emphasizing the diagnostic features of interest, and demand some understanding of the spatiotemporal physical behavior of the underlying neuronal generators. Several examples drawn from canonical epilepsy syndromes are used to illustrate this general message.

Objective To identify whether abnormal electroencephalography (EEG) connectivity is present before the onset of epileptic spasms (ES) in infants with tuberous sclerosis complex (TSC). Methods Scalp EEG recordings were collected prospectively in infants diagnosed with TSC in the first year of life. This study compared the earliest recorded EEG from infants prior to ES onset (n = 16) and from infants who did not develop ES (n = 28). Five minutes of stage II or quiet sleep was clipped and filtered into canonical EEG frequency bands. Mutual information values between each pair of EEG channels were compared directly and used as a weighted graph to calculate graph measures of global efficiency, characteristic path length, average clustering coefficient, and modularity. Results At the group level, infants who later developed ES had increased EEG connectivity in sleep. They had higher mutual information values between most EEG channels in all frequency bands adjusted for age. Infants who later developed ES had higher global efficiency and average clustering coefficients, shorter characteristic path lengths, and lower modularity across most frequency bands adjusted for age. This suggests that infants who went on to develop ES had increased local and long-range EEG connectivity with less segregation of graph regions into distinct modules. Significance This study suggests that increased neural connectivity precedes clinical ES onset in a cohort of infants with TSC. Overconnectivity may reflect progressive pathologic network synchronization culminating in generalized ES. Further research is needed before scalp EEG connectivity measures can be used as a potential biomarker of ES risk and treatment response in pre-symptomatic infants with TSC.

Understanding how brain dynamics change with dual cognitive and motor tasks can improve our knowledge of human neurophysiology. The primary goals of this study were to: (1) assess the feasibility of extracting electrocortical signals from scalp EEG while performing sustained, physically demanding dual-task walking and (2) test hypotheses about how the P300 event-related potential is affected by walking physical exertion. Participants walked on a treadmill for an hour either carrying an empty rucksack or one filled with 40% of their body weight. During the walking conditions and during a seated control condition, subjects periodically performed a visual oddball task. We recorded scalp EEG and examined electrocortical dynamics time-locked to the target stimulus. Channel-level event-related potential analysis demonstrated that it is feasible to extract reliable signals during long duration loaded walking. P300 amplitude was reduced during loaded walking versus seated, but there was no effect of time on task. Source level activity and frequency analysis revealed that sensorimotor, parietal, and cingulate brain areas all contributed to the reduced P300 amplitude during dual-task walking. We interpret the results as supporting a prioritization of cortical resources for walking, leading to fewer resources being directed toward the oddball task during dual-task locomotion.

Aim To assess whether stimulus-induced modifications of electromyographic activity observed on scalp EEG have a prognostic value in comatose patients after cardiac arrest. Methods 184 adult patients from a multi-centric prospective register who underwent an early EEG after cardiac arrest were included. Auditory and somatosensory stimulation was performed during EEG-recording. EEG reactivity (EEG-R) and EMG reactivity (EMG-R) were retrospectively assessed visually by board-certified electroencephalographers, and compared with clinical outcome (cerebral performance category, CPC) at three months. A favorable functional outcome was defined as CPC 1–2, an unfavorable outcome as CPC 3–5. Results Both EEG-R and EMG-R were predictors for good outcome (EEG-R accuracy 72% (95%-CI 66–79), sensitivity 86% (78–93), specificity 60% (50–69); EMG-R accuracy 65% (58–72), sensitivity 61% (51–75), specificity 69% (60–78)). When reactivity was defined as EEG-R and/or EMG-R, the accuracy was 73% (67–70), the sensitivity 94% (90–99), and the specificity 53% (43–63). Conclusion Taking EMG into account when assessing reactivity of EEG seems to reduce false negative predictions for identifying patients with favorable outcome after cardiac arrest.

The interference of artefacts with evoked scalp electroencephalogram (EEG) responses is a problem in event related brain computer interface (BCI) system that reduces signal quality and interpretability of user's intentions. Many strategies have been proposed to reduce the effects of non-neural artefacts, while the activity of neural sources that do not reflect the considered stimulation has been neglected. However discerning such activities from those to be retained is important, but subtle and difficult as most of their features are the same. We propose an automated method based on a combination of a genetic algorithm (GA) and a support vector machine (SVM) to select only the sources of interest. Temporal, spectral, wavelet, autoregressive and spatial properties of independent components (ICs) of EEG are inspected. The method selects the most distinguishing subset of features among this comprehensive fused set of information and identifies the components to be preserved. EEG data were recorded from 12 healthy subjects in a visual evoked potential (VEP) based BCI paradigm and the corresponding ICs were classified by experts to train and test the algorithm. They were contaminated with different sources of artefacts, including electromyogram (EMG), electrode connection problems, blinks and electrocardiogram (ECG), together with neural contributions not related to VEPs. The accuracy of ICs classification was about 88.5% and the energetic residual error in recovering the clean signals was 3%. These performances indicate that this automated method can effectively identify and remove main artefacts derived from either neural or non-neural sources while preserving VEPs. This could have important potential applications, contributing to speed and remove subjectivity of the cleaning procedure by experts. Moreover, it could be included in a real time BCI as a pre-processing step before the identification of the user’s intention.

Lack of sleep causes many sleep disorders such as nocturnal frontal lobe epilepsy, narcolepsy, bruxism, sleep apnea, insomnia, periodic limb movement disorder, and rapid eye movement behavioral disorder. Out of all, bruxism is a common behavior, which is found in 8-31% of the population. Bruxism is a sleep disorder in which individuals involuntarily grinds and clenches the teeth. The main aim of this work is to detect sleep bruxism by analyzing the electroencephalogram (EEG) spectrum analysis of the change in the domain of different stages of sleep. The present research was performed in different stages such as collection of the data, preprocessing of the EEG signal, analysis of the C4-P4 and C4-A1 channels, comparison between healthy humans and bruxism patients, and classification using decision tree method. In this study, the channels C4-P4 and C4-A1 of the EEG signal were combined for the detection of bruxism by using Welch technique, which mainly focused on two sleep stages such as S1 and rapid eye movement. The total number of EEG channels of healthy humans and bruxism patients analyzed in this work were 15 and 18, respectively. The results showed that the individual accuracy of the C4-P4 and C4-A1 channels was 81.70% and 74.11%, respectively. The combined accuracy of both C4-P4 and C4-A1 channels was 81.25%. The specificity of combined result was higher than individual. In addition, the value of theta activity during detection is consistent throughout the period, and the accuracy of S1 stage is better than rapid eye movement stage. We proposed that the theta activity of S1 could be taken for the detection of bruxism. The proposed approach in the detection of the bruxism is negligible in noise as it is in mathematical form and has taken very less time as compared with the traditional systems. The present research work would provide a fast and effective detection system of the sleep bruxism with high accuracy for medical big data applications.

Objective High frequency oscillations (HFO) between 80–500 Hz are markers of epileptic areas in intracranial and maybe also scalp EEG. We investigate simultaneous recordings of scalp and intracranial EEG and hypothesize that scalp HFOs provide important additional clinical information in the presurgical setting. Methods Spikes and HFOs were visually identified in all intracranial scalp EEG channels. Analysis of correlation of event location between intracranial and scalp EEG as well as relationship between events and the SOZ and zone of surgical removal was performed. Results 24 patients could be included, 23 showed spikes and 19 HFOs on scalp recordings. In 15/19 patients highest scalp HFO rate was located over the implantation side, with 13 patients having the highest scalp and intracranial HFO rate over the same region. 17 patients underwent surgery, 7 became seizure free. Patients with poor post-operative outcome showed significantly more regions with HFO than those with seizure free outcome. Conclusions Scalp HFOs are mostly located over the SOZ. Widespread scalp HFOs are indicative of a larger epileptic network and associated with poor postsurgical outcome. Significance Analysis of scalp HFO add clinically important information about the extent of epileptic areas during presurgical simultaneous scalp and intracranial EEG recordings.

To determine if simultaneous bilateral scalp EEG (scEEG) can accurately detect a contralateral seizure onset in patients with unilateral intracranial EEG (IEEG) implantation.We evaluated 39 seizures from 9 patients with bitemporal epilepsy who underwent simultaneous scEEG and IEEG (SSIEEG). To simulate conditions of unilateral IEEG implantation with a missed contralateral seizure onset, we analyzed the IEEG recording contralateral to the seizure onset (CL- IEEG), in conjunction with simultaneous scEEG. The following criteria were evaluated between scEEG and CL- IEEG (1) latency: the time to onset of EEG seizure (2) location: concordance of ictal onset zones and (3) pattern: congruence of EEG morphology and frequency.SSIEEG correctly lateralized 36/39 (92.3%) seizures compared to 13/39 (33.3%) seizures using CL- IEEG alone (OR = 24.0, p 0.01), 33 (84.6%) seizures using scEEG alone (OR = 2.2, p = 0.29) and 26 (66.9%) seizures using time of clinical onset alone (OR = 6.0, p = 0.01). For the three criteria evaluated, (1) 22/39 (56.4%) seizures had an earlier onset on the scEEG, compared to CL- IEEG; (2) lack of congruence of location of seizure onset was noted in 33/39 (84.6%) of the seizures; and (3) 22/39 (56.4%) seizures did not have a congruent ictal pattern.The chronological, topographic and morphologic features of SSIEEG can accurately detect the hemisphere of seizure onset in most cases with unilateral IEEG implantation. SSIEEG is significantly better than, IEEG, scEEG or clinical onset alone in this scenario. We propose that SSIEEG should be considered in all cases of intractable focal epilepsy undergoing unilateral IEEG evaluation.