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Epilepsy is a neurological disorder for which the electroencephalogram (EEG) is the most important diagnostic tool. In particular, this diagnosis heavily depends on the detection of interictal (between seizures) paroxysmal epileptic discharges (IPED) in the EEG. This is a time-consuming task requiring significant training and experience. Automatic detection of these EEG patterns would greatly assist visual inspections of human readers. We present a new method, which allows automatic detection of IPED based on discrete wavelet decomposition and a random forest classifier. The algorithm was trained and cross validated using 17 subjects with scalp EEG and 10 subjects with intracranial EEG. The performance of this method reached 62% recall and 26% precision for surface EEG subjects and 63% recall and 53% precision for intracranial EEG subjects. Thus, the method hereby proposed has great potential for diagnosis support in clinical environments.

International audience; ObjectivesNeurofeedback is a technique that aims to teach a subject to regulate a brain parameter measured by a technical interface to modulate his/her related brain and cognitive activities. However, the use of neurofeedback as a therapeutic tool for psychiatric disorders remains controversial. The aim of this review is to summarize and to comment the level of evidence of electroencephalogram (EEG) neurofeedback and real-time functional magnetic resonance imaging (fMRI) neurofeedback for therapeutic application in psychiatry.MethodLiterature on neurofeedback and mental disorders but also on Brain Computer Interfaces (BCI) used in the field of neurocognitive science has been considered by the group of expert of the NExT (Neurofeedback Evaluation & Training) section of the French Association of Biological Psychiatry and Neuropsychopharmacology (AFPBN).ResultsResults show a potential efficacy of EEG-neurofeedback in the treatment of attentional-deficit/hyperactivity disorder (ADHD) in children, even if this is still debated. For other mental disorders, there is too limited research to warrant the use of EEG-neurofeedback in clinical practice. Regarding fMRI-neurofeedback, the level of evidence remains too weak, for now, to justify clinical use. The literature review highlights various unclear points, such as indications (psychiatric disorders, pathophysiologic rationale), protocols (brain signals targeted, learning characteristics), and techniques (EEG, fMRI, signal processing). ConclusionThe field of neurofeedback involves psychiatrists, neurophysiologists and researchers in the field of brain-computer-interfaces. Future studies should determine the criteria for optimizing neurofeedback sessions. A better understanding of the learning processes underpinning neurofeedback could be a key element to develop the use of this technique in clinical practice.; IntroductionLe neurofeedback consiste à mesurer, chez un sujet, une activité cérébrale et à traiter le signal au moyen d’une interface technique afin d’en extraire un paramètre d’intérêt qui sera présenté en temps réel au participant sous la forme d’une information visuelle ou auditive. L’objectif est d’apprendre au sujet à modifier ce paramètre et donc à moduler son activité cérébrale et cognitive. Cependant, l’utilisation du neurofeedback en pratique clinique pour la prise en charge des troubles psychiatriques reste controversée.MéthodeCet article présente une synthèse de la 1re journée nationale sur le neurofeedback organisé par la section Neurofeedback Evaluation & Training (NExT) de l’Association française de psychiatrie biologique et de neuropharmacologie (AFPBN). Un état des lieux de l’utilisation du neurofeedback en électroencéphalographie (EEG) et en imagerie par résonance magnétique fonctionnelle (IRMf) est proposé. Pour intégrer l’arsenal thérapeutique, cette technique doit en effet répondre aux exigences de l’evidence based medicine.RésultatsLes études montrent une efficacité probable du neurofeedback en EEG pour le trouble du déficit de l’attention/hyperactivité (TDAH) chez les enfants. Pour les autres troubles psychiatriques, le nombre d’études est encore trop limité pour se positionner. En ce qui concerne le neurofeedback en IRMf, le niveau de preuve reste, pour l’heure, trop faible pour justifier une utilisation clinique. Les modalités d’emploi du neurofeedback, notamment en ce qui concerne les indications médicales, les protocoles d’utilisation (activité(s) cérébrale(s) ciblée(s), caractéristiques d’apprentissage) et les outils de mesure employés (EEG, IRMf, mode de traitement du signal) restent donc à clarifier.ConclusionLe vaste champ de recherche du neurofeedback implique à la fois des psychiatres, des neurophysiologistes et des chercheurs du domaine des interfaces cerveaux-ordinateurs. Les futurs travaux devront s’attacher à déterminer les critères permettant d’optimiser les séances de neurofeedback afin de mieux comprendre ses effets, le tout dans l’optique d’une utilisation en pratique clinique dans certaines indications. L’étude des processus d’apprentissage constitue un élément clé autour duquel les futures recherches devront se focaliser.

In recent years, new recording technologies have advanced such that, at high temporal and spatial resolutions, high-frequency oscillations (HFO) can be recorded in human partial epilepsy. However, because of the deluge of multichannel data generated by these experiments, achieving the full potential of parallel neuronal recordings depends on the development of new data mining techniques to extract meaningful information relating to time, frequency and space. Here, we aim to bridge this gap by focusing on up-to-date recording techniques for measurement of HFO and new analysis tools for their quantitative assessment. In particular, we emphasize how these methods can be applied, what property might be inferred from neuronal signals, and potentially productive future directions.

Epilepsy, a neurological disorder in which patients suffer from recurring seizures, affects approximately 1% of the world population. In spite of available drug and surgical treatment options, more than 25% of individuals with epilepsy have seizures that are uncontrollable. For these patients with intractable epilepsy, the unpredictability of seizure occurrence underlies an enhanced risk of sudden unexpected death or morbidity. A system that could warn the patient of the impending event or trigger an antiepileptic device would dramatically increase the quality of life for those patients. Here, we proposed a patient-specific algorithm for possible seizure warning using machine learning classification of 34 algorithmic features derived from EEG–ECG recordings. We evaluated our algorithm on unselected and continuous recordings of 12 patients (total of 108 seizures and 3178-h). Good out-of-sample performances were observed around 25% of the patients with an average preictal period around 30 min and independently of the EEG type (scalp or intracranial). Inspection of the most discriminative EEG–ECG features revealed that good classification rates reflected specific physiological precursors, particularly related to certain stages of sleep. From these observations, we conclude that our algorithmic strategy enables a quantitative way to identify “pro-ictal” states with a high risk of seizure generation.

Interrupting a focal, chronic infusion of GABA to the rat motor cortex initiates the progressive emergence of a sustained spiking electroencephalographic (EEG) activity, associated with myoclonic jerks of the corresponding body territory. This activity is maintained over several hours, has an average frequency of 1.5 Hz, is localized to the infusion site and never generalizes. The GABA withdrawal syndrome (GWS) has therefore features of partial status epilepticus. Changes in EEG signals associated with the GWS were studied in freely moving rats by measuring the phase synchrony between bilateral epidural records from the neocortex. Our results showed (i) epileptic activity was associated with a striking decrease in phase synchrony between all pairs of electrodes including the focus, predominantly in the 1–6 Hz frequency range. There was a mean decrease of 75.34±5.26% in phase synchrony levels between the period before GABA interruption and the period after epileptic activity appeared. (ii) This reduction in synchrony contrasted with an increase of power spectral density in the corresponding EEG channels over the same 1–6 Hz frequency range, (iii) neither changes in synchrony nor in nonlinear dynamics were detected before the first EEG spikes, (iv) systemic injection of ketamine, an antagonist of N-methyl- d -aspartic acid (NMDA) receptors, modified transiently both epileptic activity and the synchrony profile. (v) Spiking activity and synchrony changes were suppressed by reperfusion of GABA. Our data suggest that, during a partial status epilepticus, interactions between the epileptic focus and connected neocortical neuronal populations are dramatically decreased in low frequencies.

The mechanisms underlying the transition of brain activity toward epileptic seizures remain unclear. Based on nonlinear analysis of both intracranial and scalp electroencephalographic (EEG) recordings, different research groups have recently reported dynamical smooth changes in epileptic brain activity several minutes before seizure onset. Such preictal states have been detected in populations of patients with mesial temporal lobe epilepsy (MTLE) and, more recently, with different neocortical partial epilepsies (NPEs). In this paper, we are particularly interested in the spatio-temporal organization of epileptogenic networks prior to seizures in neocortical epilepsies. For this, we characterize the network of two patients with NPE by means of two nonlinear measures of interdependencies. Since the synchronization of neuronal activity is an essential feature of the generation and propagation of epileptic activity, we have analyzed changes in phase synchrony between EEG time series. In order to compare the phase and amplitude dynamics, we have also studied the degree of association between pairs of signals by means of a nonlinear correlation coefficient. Recent findings have suggested changes prior to seizures in a wideband frequency range. Instead, for the examples of this study, we report a significant decrease of synchrony in the focal area several minutes before seizures (/spl Gt/30 min in both patients) in the frequency band of 10-25 Hz mainly. Furthermore, the spatio-temporal organization of this preictal activity seems to be specifically related to this frequency band. Measures of both amplitude and phase coupling yielded similar results in narrow-band analysis. These results may open new perspectives on the mechanisms of seizure emergence as well as the organization of neocortical epileptogenic networks. The possibility of forecasting the onset of seizures has important implications for a better understanding, diagnosis and a potential treatment of the epilepsy.

Recent studies have shown that non-linear analysis of intracranial activities can detect a ‘pre-ictal phase’ preceding the epileptic seizure. Nevertheless, the dynamical nature of the underlying neuronal process and the spatial extension of this pre-ictal phase still remain unknown. In this paper, we address these aspects using a new non-linear measure of dynamic similarity between different parts of intracranial recordings of nine patients with medial temporal lobe epilepsy recorded during transitions to seizure. Our results confirm that non-linear changes in neuronal dynamics allow, in most cases (16 out of 17), a seizure anticipation several minutes in advance. Furthermore, we show that the spatial distribution of pre-ictal changes often involves an extended network projecting beyond the limits of the epileptogenic region. Finally, the pre-ictal phase could frequently (13 out of 17) be characterized with a marked shift toward slower frequencies in upper delta or theta frequency range.

Epileptic seizures are a principal brain dysfunction with important public health implications, as they affect 0.8% of humans. Many of these patients (20%) are resistant to treatment with drugs1. The ability to anticipate the onset of seizures in such cases would permit clinical interventions. The view of chronic focal epilepsy now is that abnormally discharging neurons act as pacemakers to recruit and entrain other normal neurons by loss of inhibition and synchronization into a critical mass2. Thus, pre-ictal changes should be detectable during the stages of recruitment. Traditional signal analyses, such as the count of focal spike density3, the frequency coherence4 or spectral analyses are not reliable predictors. Non-linear indicators may undergo consistent changes around seizure onset5,6,7. Our objective was to follow the transition into seizure by reconstructing intracranial recordings in implanted patients as trajectories in a phase space and then introduce non-linear indicators to characterize them8,9. These indicators take into account the extended spatio–temporal nature of the epileptic recruitment processes10 and the corresponding physiological events governed by short-term causalities in the time series. We demonstrate that in most cases (17 of 19), seizure onset could be anticipated well in advance (between 2–6 minutes beforehand), and that all subjects seemed to share a similar 'route' towards seizure.

Recent evidence suggests that some seizures are preceded by preictal changes that start from minutes to hours before an ictal event. Nevertheless an adequate statistical evaluation in a large database of continuous multiday recordings is still missing. Here, we investigated the existence of preictal changes in long-term intracranial recordings from 53 patients with intractable partial epilepsy (in total 531 days and 558 clinical seizures). We describe a measure of brain excitability based on the slow modulation of high-frequency gamma activities (40–140 Hz) in ensembles of intracranial contacts. In prospective tests, we found that this index identified preictal changes at levels above chance in 13.2% of the patients (7/53), suggesting that results may be significant for the whole group (p < 0.05). These results provide a demonstration that preictal states can be detected prospectively from EEG data. They advance understanding of the network dynamics leading to seizure and may help develop novel seizure prediction algorithms.

During an ordinary night, the human body goes through various sleep stages, most commonly classified as REM and non-REM sleep cycles. These cycles have been studied and characterized by means of polysomnography studies, and have revealed different brain activities visible in the EEG recording. From this, the slow waves, a hallmark for the non-REM sleep stage, have been associated with important functional processes, such as long term memory storage, synaptic regulation and plasticity. Abnormalities in the electrical expression of such slows waves could be an indicator of long term deficiencies in these functional process as a consequence of some neurological disorders like epilepsy. In this work, we have studied different characteristics measured from the electrical expression of slow waves between normal and epileptic subjects. These parameters allowed us to determine significant differences in the preferential depolarization and hyperpolarization times, suggesting a possible alterations in the underlying neuronal processes.

A Matlab®-based software package, EPILAB, was developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface. Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented. This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. Seizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines. EPILAB proved to be an efficient tool for seizure prediction, and aims to be a way to communicate, evaluate, and compare results and data among the seizure prediction community.

The daily life of epilepsy patients is constrained by the possibility of occurrence of seizures. Until now, seizures cannot be predicted with sufficient sensitivity and specificity. Most of the seizure prediction studies have been focused on a small number of patients, and frequently assuming unrealistic hypothesis. This paper adopts the view that for an appropriate development of reliable predictors one should consider long-term recordings and several features and algorithms integrated in one software tool. A computational environment, based on Matlab (®), is presented, aiming to be an innovative tool for seizure prediction. It results from the need of a powerful and flexible tool for long-term EEG/ECG analysis by multiple features and algorithms. After being extracted, features can be subjected to several reduction and selection methods, and then used for prediction. The predictions can be conducted based on optimized thresholds or by applying computational intelligence methods. One important aspect is the integrated evaluation of the seizure prediction characteristic of the developed predictors.

Epilepsy, a neurological disorder in which patients suffer from recurring seizures, affects approximately 1% of the world population. In spite of available drug and surgical treatment options, more than 25% of individuals with epilepsy have seizures that are uncontrollable. For these patients with intractable epilepsy, the unpredictability of seizure occurrence underlies an enhanced risk of sudden unexpected death or morbidity. Therefore, a device that could predict a seizure and notify the patient of the impending event or trigger an antiepileptic device would dramatically increase the quality of life for those patients. Here, a patient-specific classification algorithm is proposed to distinguish between preictal and interictal features extracted from ECG-EEG recordings. It demonstrates that the classifier based on a Support Vector Machine (SVM) can distinguish preictal from interictal with a high degree of sensitivity and specificity. The proposed algorithm was applied to long-term recordings of 4 patients with partial epilepsy, totaling 29 seizures and more than 1333-hour-long interictal, and it produced average sensitivity and specificity values of 90.6% and 85.6% respectively using 10-minute-long window of preictal recording.

In a previous publication we showed that non-linear analysis can extract spatio-temporal changes of brain electrical activity prior to epileptic seizures. Here we describe a new method to analyze this long-term non-stationarity in the EEG by a measure of dynamical similarity between different parts of the time series. We apply this method to the study of a group of patients with temporal lobe epilepsy recorded intracranially during transitions to seizure. We show that the method, which can be implemented on a personal computer, can track in real time spatio-temporal changes in brain dynamics several minutes prior to seizure.

The need of a reliable seizure prediction is motivated by the 50 million people in the world suffering from epilepsy, of whom 30% have no control on seizures with current pharmacological treatments. Seizure prediction research holds great promise for such patients, since an effective algorithm will enable the development of a closed-loop system that intervenes before the clinical onset of a seizure. As a step toward practical implementation of this technology, we present a new method based on a measure of brain excitability identified by couplings between low-frequency phases and high-frequency amplitudes of brain oscillations. The proposed method was applied to long-term intracranial recordings of 20 patients with partial epilepsy, for a total of 267 seizures and more than 3400-hour-long interictal activities. We found that our predictor was in 50% of cases better than chance, with an average sensitivity of 98.9% and false prediction rate of 1.84/hour. From these observations, we concluded that our method enables a new quantitative way to identify preictal states with a high risk of seizure generation.