Trends in the use of automated algorithms for the detection of high-frequency oscillations associated with human epilepsy.

High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the epileptogenic zone and tool for surgical planning. Many studies have shown that a high rate of HFOs (number per minute) is correlated with the seizure-onset zone, and complete removal of HFO-generating brain regions has been associated with seizure-free outcome after surgery. In order to use HFOs as a biomarker, these transient events must first be detected in electrophysiological data. Because visual detection of HFOs is time-consuming and subject to low interrater reliability, many automated algorithms have been developed, and they are being used increasingly for such studies. However, there is little guidance on how to select an algorithm, implement it in a clinical setting, and validate the performance. Therefore, we aim to review automated HFO detection algorithms, focusing on conceptual similarities and differences between them. We summarize the standard steps for data pre-processing, as well as post-processing strategies for rejection of false-positive detections. We also detail four methods for algorithm testing and validation, and we describe the specific goal achieved by each one. We briefly review direct comparisons of automated algorithms applied to the same data set, emphasizing the importance of optimizing detection parameters. Then, to assess trends in the use of automated algorithms and their potential for use in clinical studies, we review evidence for the relationship between automatically detected HFOs and surgical outcome. We conclude with practical recommendations and propose standards for the selection, implementation, and validation of automated HFO-detection algorithms.

Long-Range Temporal Correlations Reflect Treatment Response in the Electroencephalogram of Patients with Infantile Spasms.

Infantile spasms syndrome is an epileptic encephalopathy in which prompt diagnosis and treatment initiation are critical to therapeutic response. Diagnosis of the disease heavily depends on the identification of characteristic electroencephalographic (EEG) patterns, including hypsarrhythmia. However, visual assessment of the presence and characteristics of hypsarrhythmia is challenging because multiple variants of the pattern exist, leading to poor inter-rater reliability. We investigated whether a quantitative measurement of the control of neural synchrony in the EEGs of infantile spasms patients could be used to reliably distinguish the presence of hypsarrhythmia and indicate successful treatment outcomes. We used autocorrelation and Detrended Fluctuation Analysis (DFA) to measure the strength of long-range temporal correlations in 21 infantile spasms patients before and after treatment and 21 control subjects. The strength of long-range temporal correlations was significantly lower in patients with hypsarrhythmia than control patients, indicating decreased control of neural synchrony. There was no difference between patients without hypsarrhythmia and control patients. Further, the presence of hypsarrhythmia could be classified based on the DFA exponent and intercept with 92% accuracy using a support vector machine. Successful treatment was marked by a larger increase in the DFA exponent compared to those in which spasms persisted. These results suggest that the strength of long-range temporal correlations is a marker of pathological cortical activity that correlates with treatment response. Combined with current clinical measures, this quantitative tool has the potential to aid objective identification of hypsarrhythmia and assessment of treatment efficacy to inform clinical decision-making.

A probabilistic method for determining cortical dynamics during seizures.

This work presents a probabilistic method for inferring the parameter ranges in a biologically relevant mathematical model of the cortex most likely to be producing seizures observed in an electrocorticogram (ECoG) signal from a human subject. Additionally, this method produces a probabilistic pathway of the temporal evolution of physiological state in the cortex over the course of individual seizures, leveraging a model of the cortex that describes cortical physiology. We describe ways in which these methods and results offer insights into seizure etiology and have the potential to suggest new treatment options. To directly account for the stochastic and noisy nature of the mathematical model and the ECoG signal, we use a probabilistic Bayesian framework to map features of ECoG segments onto a distribution of likelihoods over physiologically-relevant parameter states. A Hidden Markov Model (HMM) is then introduced to incorporate the belief that cortical physiology has both temporal continuity and also a degree of reproducibility between individual seizures. By inspecting the ratio of likelihoods between HMMs run under two possible parameter regions, both of which produce seizures in the model, we determine which physiological parameter regions are more likely to be causing the observed seizures. We show that between individual seizures, there is consistency in these likelihood ratios between hypothesized regions, in the temporal pathways calculated, and in the separation of seizure from non-seizure time segment likelihood maps.

A probabilistic framework for a physiological representation of dynamically evolving sleep state.

This work presents a probabilistic method for mapping human sleep electroencephalogram (EEG) signals onto a state space based on a biologically plausible mathematical model of the cortex. From a noninvasive EEG signal, this method produces physiologically meaningful pathways of the cortical state over a night of sleep. We propose ways in which these pathways offer insights into sleep-related conditions, functions, and complex pathologies. To address explicitly the noisiness of the EEG signal and the stochastic nature of the mathematical model, we use a probabilistic Bayesian framework to map each EEG epoch to a distribution of likelihoods over all model sleep states. We show that the mapping produced from human data robustly separates rapid eye movement sleep (REM) from slow wave sleep (SWS). A Hidden Markov Model (HMM) is incorporated to improve the path results using the prior knowledge that cortical physiology has temporal continuity.

Optimizing automated detection of high frequency oscillations using visual markings does not improve SOZ localization.

OBJECTIVE: High frequency oscillations (HFOs) are a biomarker of the seizure onset zone (SOZ) and can be visually or automatically detected. In theory, one can optimize an automated algorithm's parameters to maximize SOZ localization accuracy; however, there is no consensus on whether or how this should be done. Therefore, we optimized an automated detector using visually identified HFOs and evaluated the impact on SOZ localization accuracy. METHODS: We detected HFOs in intracranial EEG from 20 patients with refractory epilepsy from two centers using (1) unoptimized automated detection, (2) visual identification, and (3) automated detection optimized to match visually detected HFOs. RESULTS: SOZ localization accuracy based on HFO rate was not significantly different between the three methods. Across patients, visually optimized detector settings varied, and no single set of settings produced universally accurate SOZ localization. Exploratory analysis suggests that, for many patients, detection settings exist that would improve SOZ localization. CONCLUSIONS: SOZ localization accuracy was similar for all three methods, was not improved by visually optimizing detector settings, and may benefit from patient-specific parameter optimization. SIGNIFICANCE: Visual HFO marking is laborious, and optimizing automated detection using visual markings does not improve localization accuracy. New patient-specific detector optimization methods are needed.

Interrater reliability of interictal EEG waveforms in Lennox-Gastaut Syndrome.

OBJECTIVE: Identification of EEG waveforms is critical for diagnosing Lennox-Gastaut Syndrome (LGS) but is complicated by the progressive nature of the disease. Here, we assess the interrater reliability (IRR) among pediatric epileptologists for classifying EEG waveforms associated with LGS. METHODS: A novel automated algorithm was used to objectively identify epochs of EEG with transient high power, which were termed events of interest (EOIs). The algorithm was applied to EEG from 20 LGS subjects and 20 healthy controls during NREM sleep, and 1350 EOIs were identified. Three raters independently reviewed the EOIs within isolated 15-second EEG segments in a randomized, blinded fashion. For each EOI, the raters assigned a waveform label (spike and slow wave, generalized paroxysmal fast activity, seizure, spindle, vertex, muscle, artifact, nothing, or other) and indicated the perceived subject type (LGS or control). RESULTS: Labeling of subject type had 85% accuracy across all EOIs and an IRR of κ =0.790, suggesting that brief segments of EEG containing high-power waveforms can be reliably classified as pathological or normal. Waveform labels were less consistent, with κ =0.558, and the results were highly variable for different categories of waveforms. Label mismatches typically occurred when one reviewer selected "nothing," suggesting that reviewers had different thresholds for applying named labels. SIGNIFICANCE: Classification of EEG waveforms associated with LGS has weak IRR, due in part to varying thresholds applied during visual review. Computational methods to objectively define EEG biomarkers of LGS may improve IRR and aid clinical decision-making.

Electrode Surface Area Impacts Measurement of High Frequency Oscillations in Human Intracranial EEG.

OBJECTIVE: High-frequency oscillations (HFOs) are a promising prognostic biomarker of surgical outcome in patients with epilepsy. Their rates of occurrence and morphology have been studied extensively using recordings from electrodes of various geometries. While electrode size is a potential confounding factor in HFO studies, it has largely been disregarded due to a lack of consistent evidence. Therefore, we designed an experiment to directly test the impact of electrode size on HFO measurement. METHODS: We first simulated HFO measurement using a lumped model of the electrode-tissue interaction. Then eight human subjects were each implanted with a high-density 8x8 grid of subdural electrodes. After implantation, the electrode sizes were altered using a technique recently developed by our group, enabling intracranial EEG recordings for three different electrode surface areas from a static brain location. HFOs were automatically detected in the data and their characteristics were calculated. RESULTS: The human subject measurements were consistent with the model. Specifically, HFO rate measured per area of tissue decreased significantly as electrode surface area increased. The smallest electrodes recorded more fast ripples than ripples. Amplitude of detected HFOs also decreased as electrode surface area increased, while duration and peak frequency were unaffected. CONCLUSION: These results suggest that HFO rates measured using electrodes of different surface areas cannot be compared directly. SIGNIFICANCE: This has significant implications for HFOs as a tool for surgical planning, particularly for individual patients implanted with electrodes of multiple sizes and comparisons of HFO rate made across patients and studies.

Computational EEG attributes predict response to therapy for epileptic spasms.

OBJECTIVE: We set out to evaluate whether response to treatment for epileptic spasms is associated with specific candidate computational EEG biomarkers, independent of clinical attributes. METHODS: We identified 50 children with epileptic spasms, with pre- and post-treatment overnight video-EEG. After EEG samples were preprocessed in an automated fashion to remove artifacts, we calculated amplitude, power spectrum, functional connectivity, entropy, and long-range temporal correlations (LRTCs). To evaluate the extent to which each feature is independently associated with response and relapse, we conducted logistic and proportional hazards regression, respectively. RESULTS: After statistical adjustment for the duration of epileptic spasms prior to treatment, we observed an association between response and stronger baseline and post-treatment LRTCs (P = 0.042 and P = 0.004, respectively), and higher post-treatment entropy (P = 0.003). On an exploratory basis, freedom from relapse was associated with stronger post-treatment LRTCs (P = 0.006) and higher post-treatment entropy (P = 0.044). CONCLUSION: This study suggests that multiple EEG features-especially LRTCs and entropy-may predict response and relapse. SIGNIFICANCE: This study represents a step toward a more precise approach to measure and predict response to treatment for epileptic spasms.

A novel method for dynamically altering the surface area of intracranial EEG electrodes.

Objective.Intracranial electroencephalogram (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinson's disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulations, as well as experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain.Approach.We first present a theoretical model and anin vitrovalidation of the method. We then report the results of anin vivoimplementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e. epileptic spikes.Main Results.We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation did not change significantly with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in larger electrodes. This likely depends on the precise location and spatial spread of each spike.Significance.Overall, this new method enables multi-scale measurements of electrical activity in the human brain that can facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.

Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone.

Objective. High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO 'rate') is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases during periods of slow-wave sleep. Moreover, HFOs that are predictive of epileptic tissue may occur in oscillatory patterns due to phase coupling with lower frequencies. Therefore, we sought to further characterize between-seizure (i.e. 'interictal') HFO dynamics both within and outside the seizure onset zone (SOZ).Approach. Using long-term intracranial EEG (mean duration 10.3 h) from 16 patients, we automatically detected HFOs using a new algorithm. We then fit a hierarchical negative binomial model to the HFO counts. To account for differences in HFO dynamics and rates between sleep and wakefulness, we also fit a mixture model to the same data that included the ability to switch between two discrete brain states that were automatically determined during the fitting process. The ability to predict the SOZ by model parameters describing HFO dynamics (i.e. clumping coefficients and coefficients of variation) was assessed using receiver operating characteristic curves.Main results. Parameters that described HFO dynamics were predictive of SOZ. In fact, these parameters were found to be more consistently predictive than HFO rate. Using concurrent scalp EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM sleep and (2) awake and rapid eye movement sleep. However the brain state most likely corresponding to slow-wave sleep in the second model improved SOZ prediction compared to the first model for only some patients.Significance. This work suggests that delineation of SOZ with interictal data can be improved by the inclusion of time-varying HFO dynamics.

EEG biomarkers for the diagnosis and treatment of infantile spasms.

Early diagnosis and treatment are critical for young children with infantile spasms (IS), as this maximizes the possibility of the best possible child-specific outcome. However, there are major barriers to achieving this, including high rates of misdiagnosis or failure to recognize the seizures, medication failure, and relapse. There are currently no validated tools to aid clinicians in assessing objective diagnostic criteria, predicting or measuring medication response, or predicting the likelihood of relapse. However, the pivotal role of EEG in the clinical management of IS has prompted many recent studies of potential EEG biomarkers of the disease. These include both visual EEG biomarkers based on human visual interpretation of the EEG and computational EEG biomarkers in which computers calculate quantitative features of the EEG. Here, we review the literature on both types of biomarkers, organized based on the application (diagnosis, treatment response, prediction, etc.). Visual biomarkers include the assessment of hypsarrhythmia, epileptiform discharges, fast oscillations, and the Burden of AmplitudeS and Epileptiform Discharges (BASED) score. Computational markers include EEG amplitude and power spectrum, entropy, functional connectivity, high frequency oscillations (HFOs), long-range temporal correlations, and phase-amplitude coupling. We also introduce each of the computational measures and provide representative examples. Finally, we highlight remaining gaps in the literature, describe practical guidelines for future biomarker discovery and validation studies, and discuss remaining roadblocks to clinical implementation, with the goal of facilitating future work in this critical area.

Evolution of Cortical Functional Networks in Healthy Infants.

During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0-2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21-24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.

A continuous mapping of sleep states through association of EEG with a mesoscale cortical model.

Here we show that a mathematical model of the human sleep cycle can be used to obtain a detailed description of electroencephalogram (EEG) sleep stages, and we discuss how this analysis may aid in the prediction and prevention of seizures during sleep. The association between EEG data and the cortical model is found via locally linear embedding (LLE), a method of dimensionality reduction. We first show that LLE can distinguish between traditional sleep stages when applied to EEG data. It reliably separates REM and non-REM sleep and maps the EEG data to a low-dimensional output space where the sleep state changes smoothly over time. We also incorporate the concept of strongly connected components and use this as a method of automatic outlier rejection for EEG data. Then, by using LLE on a hybrid data set containing both sleep EEG and signals generated from the mesoscale cortical model, we quantify the relationship between the data and the mathematical model. This enables us to take any sample of sleep EEG data and associate it with a position among the continuous range of sleep states provided by the model; we can thus infer a trajectory of states as the subject sleeps. Lastly, we show that this method gives consistent results for various subjects over a full night of sleep and can be done in real time.

An Effortful Approach to Social Affiliation in Schizophrenia: Preliminary Evidence of Increased Theta and Alpha Connectivity during a Live Social Interaction.

People with schizophrenia often experience a profound lack of motivation for social affiliation-a facet of negative symptoms that detrimentally impairs functioning. However, the mechanisms underlying social affiliative deficits remain poorly understood, particularly under realistic social contexts. Here, we investigated subjective reports and electroencephalography (EEG) functional connectivity in schizophrenia during a live social interaction. Individuals with schizophrenia (n = 16) and healthy controls (n = 29) completed a face-to-face interaction with a confederate while having EEG recorded. Participants were randomly assigned to either a Closeness condition designed to elicit feelings of closeness through self-disclosure or a Small-Talk condition with minimal disclosure. Compared to controls, patients reported lower positive emotional experiences and feelings of closeness across conditions, but they showed comparably greater subjective affiliative responses for the Closeness (vs. Small-Talk) condition. Additionally, patients in the Closeness (vs. Small-Talk) condition displayed a global increase in connectivity in theta and alpha frequency bands that was not observed for controls. Importantly, greater theta and alpha connectivity was associated with greater subjective affiliative responding, greater negative symptoms, and lower disorganized symptoms in patients. Collectively, findings indicate that patients, because of pronounced negative symptoms, utilized a less efficient, top-down mediated strategy to process social affiliation.

Infant functional networks are modulated by state of consciousness and circadian rhythm.

Functional connectivity networks are valuable tools for studying development, cognition, and disease in the infant brain. In adults, such networks are modulated by the state of consciousness and the circadian rhythm; however, it is unknown if infant brain networks exhibit similar variation, given the unique temporal properties of infant sleep and circadian patterning. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings (average duration 20.8 hr) from 19 healthy infants. Networks were subject specific, as intersubject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. Principal component analysis revealed the presence of two dominant networks; visual sleep scoring confirmed that these corresponded to sleep and wakefulness. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in either wakefulness or sleep at the group level. Together, these results suggest that modulation of healthy functional networks occurs over ∼24 hr and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

Computational characteristics of interictal EEG as objective markers of epileptic spasms.

OBJECTIVE: Favorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five categories of computational electroencephalographic (EEG) measures as markers of ES. METHODS: We measured 1) amplitude, 2) power spectra, 3) Shannon entropy and permutation entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity using cross-correlation and phase lag index (PLI). EEG data were analyzed from ES patients (n = 40 patients) and healthy controls (n = 20 subjects), with multiple blinded measurements during wakefulness and sleep for each patient. RESULTS: In ES patients, EEG amplitude was significantly higher in all electrodes when compared to controls. Shannon and permutation entropy were lower in ES patients than control subjects. The DFA intercept values in ES patients were significantly higher than control subjects, while DFA exponent values were not significantly different between the groups. EEG functional connectivity networks in ES patients were significantly stronger than controls when based on both cross-correlation and PLI. Significance for all statistical tests was p < 0.05, adjusted for multiple comparisons using the Benjamini-Hochberg procedure as appropriate. Finally, using logistic regression, a multi-attribute classifier was derived that accurately distinguished cases from controls (area under curve of 0.96). CONCLUSIONS: Computational EEG features successfully distinguish ES patients from controls in a large, blinded study. SIGNIFICANCE: These objective EEG markers, in combination with other clinical factors, may speed the diagnosis and treatment of the disease, thereby improving long-term outcomes.

Scalp EEG markers of normal infant development using visual and computational approaches.

The infant brain is rapidly developing, and these changes are reflected in scalp electroencephalography (EEG) features, including power spectrum and sleep spindle characteristics. These biomarkers not only mirror infant development, but they are also altered by conditions such as epilepsy, autism, developmental delay, and trisomy 21. Prior studies of early development were generally limited by small cohort sizes, lack of a specific focus on infancy (0-2 years), and exclusive use of visual marking for sleep spindles. Therefore, we measured the EEG power spectrum and sleep spindles in 240 infants ranging from 0-24 months. To rigorously assess these metrics, we used both clinical visual assessment and computational techniques, including automated sleep spindle detection. We found that the peak frequency and power of the posterior dominant rhythm (PDR) increased with age, and a corresponding peak occurred in the EEG power spectra. Based on both clinical and computational measures, spindle duration decreased with age, and spindle synchrony increased with age. Our novel metric of spindle asymmetry suggested that peak spindle asymmetry occurs at 6-9 months of age.Clinical Relevance- Here we provide a robust characterization of the development of EEG brain rhythms during infancy. This can be used as a basis of comparison for studies of infant neurological disease, including epilepsy, autism, developmental delay, and trisomy 21.

Automated detection of ripple oscillations in long-term scalp EEG from patients with infantile spasms.

Objective.Scalp high-frequency oscillations (HFOs) are a promising biomarker of epileptogenicity in infantile spasms (IS) and many other epilepsy syndromes, but prior studies have relied on visual analysis of short segments of data due to the prevalence of artifacts in EEG. Here we set out to robustly characterize the rate and spatial distribution of HFOs in large datasets from IS subjects using fully automated HFO detection techniques.Approach.We prospectively collected long-term scalp EEG data from 12 subjects with IS and 18 healthy controls. For patients with IS, recording began prior to diagnosis and continued through initiation of treatment with adrenocorticotropic hormone (ACTH). The median analyzable EEG duration was 18.2 h for controls and 84.5 h for IS subjects (∼1300 h total). Ripples (80-250 Hz) were detected in all EEG data using an automated algorithm.Main results.HFO rates were substantially higher in patients with IS compared to controls. In IS patients, HFO rates were higher during sleep compared to wakefulness (median 5.5 min-1and 2.9 min-1, respectively;p = 0.002); controls did not exhibit a difference in HFO rate between sleep and wakefulness (median 0.98 min-1and 0.82 min-1, respectively). Spatially, IS patients exhibited significantly higher rates of HFOs in the posterior parasaggital region and significantly lower HFO rates in frontal channels, and this difference was more pronounced during sleep. In IS subjects, ACTH therapy significantly decreased the rate of HFOs.Significance.Here we provide a detailed characterization of the spatial distribution and rates of HFOs associated with IS, which may have relevance for diagnosis and assessment of treatment response. We also demonstrate that our fully automated algorithm can be used to detect HFOs in long-term scalp EEG with sufficient accuracy to clearly discriminate healthy subjects from those with IS.

Automated preprocessing and phase-amplitude coupling analysis of scalp EEG discriminates infantile spasms from controls during wakefulness.

OBJECTIVE: Delta-gamma phase-amplitude coupling in EEG is useful for localizing epileptic sources and to evaluate severity in children with infantile spasms. We (1) develop an automated EEG preprocessing pipeline to clean data using artifact subspace reconstruction (ASR) and independent component (IC) analysis (ICA) and (2) evaluate delta-gamma modulation index (MI) as a method to distinguish children with epileptic spasms (cases) from normal controls during sleep and awake. METHODS: Using 400 scalp EEG datasets (200 sleep, 200 awake) from 100 subjects, we calculated MI after applying high-pass and line-noise filters (Clean 0), and after ASR followed by either conservative (Clean 1) or stringent (Clean 2) artifactual IC rejection. Classification of cases and controls using MI was evaluated with Receiver Operating Characteristics (ROC) to obtain area under curve (AUC). RESULTS: The artifact rejection algorithm reduced raw signal variance by 29-45% and 38-60% for Clean 1 and Clean 2, respectively. MI derived from sleep data, with or without preprocessing, robustly classified the groups (all AUC > 0.98). In contrast, group classification using MI derived from awake data was successful only after Clean 2 (AUC = 0.85). CONCLUSIONS: We have developed an automated EEG preprocessing pipeline to perform artifact rejection and quantify delta-gamma modulation index.

A model of feedback control for the charge-balanced suppression of epileptic seizures.

Here we present several refinements to a model of feedback control for the suppression of epileptic seizures. We utilize a stochastic partial differential equation (SPDE) model of the human cortex. First, we verify the strong convergence of numerical solutions to this model, paying special attention to the sharp spatial changes that occur at electrode edges. This allows us to choose appropriate step sizes for our simulations; because the spatial step size must be small relative to the size of an electrode in order to resolve its electrical behavior, we are able to include a more detailed electrode profile in the simulation. Then, based on evidence that the mean soma potential is not the variable most closely related to the measurement of a cortical surface electrode, we develop a new model for this. The model is based on the currents flowing in the cortex and is used for a simulation of feedback control. The simulation utilizes a new control algorithm incorporating the total integral of the applied electrical potential. Not only does this succeed in suppressing the seizure-like oscillations, but it guarantees that the applied signal will be charge-balanced and therefore unlikely to cause cortical damage.