Localizational concepts in epilepsy: past, present and future

The clinical seizure pattern, particularly the initial phenomena, plus the EEG, when satisfactory recording of the seizure onset can be achieved, determine the primary localization of epileptic phenomena. The EEG has also demonstrated, by the presence of interictal epileptiform spike discharges, the presence of a second-order localization of epileptic phenomena, namely, the location and extent of cortex adjacent to the site of origin of the neuronal seizure discharge that is recruited into action in a clinical epileptic seizure. Experience with cortical resection in the treatment of focal epilepsy has demonstrated the importance of a third-order localization of epileptic phenomena, namely, how much of the potentially epileptogenic cortex must be excised in order to produce a satisfactory reduction of the seizure tendency.     

Neurosurgical management of epilepsy: a personal perspective in 1983

The therapeutic goal in the neurosurgical treatment of medically intractable epilepsy is complete seizure control, for both biologic and psychosocial reasons. Cortical resections are more likely to accomplish this than other surgical alternatives for epilepsy. Although abnormalities on new imaging techniques (CT, positron emission scanning) aid in identifying the epileptic focus, interictal epileptiform EEG changes remain the main indicator of focal origin of the seizures. Where this is equivocal, direct brain recording of spontaneous seizures with subdural electrodes is of value in identifying the side and lobe of seizure onset. The cortical resection is then tailored by the extent of the interictal electrocorticographic abnormalities and functional identification of essential areas such as those for language, using an electrical stimulation mapping technique, under local anesthesia. With this approach, half of the patients with temporal lobe foci are seizure-free since the time of operation, over two-thirds become so with time, and over three-quarters have at least very major reductions in seizure frequency.     

Localizing confined epileptic foci in patients with an unclear focus or presumed multifocality using a component-based EEG-fMRI method

Precise localization of epileptic foci is an unavoidable prerequisite in epilepsy surgery. Simultaneous EEG-fMRI recording has recently created new horizons to locate foci in patients with epilepsy and, in comparison with single-modality methods, has yielded more promising results although it is still subject to limitations such as lack of access to information between interictal events. This study assesses its potential added value in the presurgical evaluation of patients with complex source localization. Adult candidates considered ineligible for surgery on account of an unclear focus and/or presumed multifocality on the basis of EEG underwent EEG-fMRI. Adopting a component-based approach, this study attempts to identify the neural behavior of the epileptic generators and detect the components-of-interest which will later be used as input in the GLM model, substituting the classical linear regressor. Twenty-eight sets interictal epileptiform discharges (IED) from nine patients were analyzed. In eight patients, at least one BOLD response was significant, positive and topographically related to the IEDs. These patients were rejected for surgery because of an unclear focus in four, presumed multifocality in three, and a combination of the two conditions in two. Component-based EEG-fMRI improved localization in five out of six patients with unclear foci. In patients with presumed multifocality, component-based EEG-fMRI advocated one of the foci in five patients and confirmed multifocality in one of the patients. In seven patients, component-based EEG-fMRI opened new prospects for surgery and in two of these patients, intracranial EEG supported the EEG-fMRI results. In these complex cases, component-based EEG-fMRI either improved source localization or corroborated a negative decision regarding surgical candidacy. As supported by the statistical findings, the developed EEG-fMRI method leads to a more realistic estimation of localization compared to the conventional EEG-fMRI approach, making it a tool of high value in pre-surgical evaluation of patients with refractory epilepsy. To ensure proper implementation, we have included guidelines for the application of component-based EEG-fMRI in clinical practice.     

Multiway analysis of epilepsy tensors

MOTIVATION: The success or failure of an epilepsy surgery depends greatly on the localization of epileptic focus (origin of a seizure). We address the problem of identification of a seizure origin through an analysis of ictal electroencephalogram (EEG), which is proven to be an effective standard in epileptic focus localization. SUMMARY: With a goal of developing an automated and robust way of visual analysis of large amounts of EEG data, we propose a novel approach based on multiway models to study epilepsy seizure structure. Our contributions are 3-fold. First, we construct an Epilepsy Tensor with three modes, i.e. time samples, scales and electrodes, through wavelet analysis of multi-channel ictal EEG. Second, we demonstrate that multiway analysis techniques, in particular parallel factor analysis (PARAFAC), provide promising results in modeling the complex structure of an epilepsy seizure, localizing a seizure origin and extracting artifacts. Third, we introduce an approach for removing artifacts using multilinear subspace analysis and discuss its merits and drawbacks. RESULTS: Ictal EEG analysis of 10 seizures from 7 patients are included in this study. Our results for 8 seizures match with clinical observations in terms of seizure origin and extracted artifacts. On the other hand, for 2 of the seizures, seizure localization is not achieved using an initial trial of PARAFAC modeling. In these cases, first, we apply an artifact removal method and subsequently apply the PARAFAC model on the epilepsy tensor from which potential artifacts have been removed. This method successfully identifies the seizure origin in both cases.     

癫痫病人脑电信号的奇异谱

癫痫是一种常见的神经系统疾患,其唯一客观证据为脑电图的癫痫样发放。在癫痫发作间期,仅有偶发的很难辨别的癫痫样放电,为了正确诊断癫痫病,往往需要医生长时间监测病人的脑电信号,在对脑电信号进行相空间重构,进而对其进行奇异系统分析,发现癫痫病人无论在癫痫发作前、发作中、发作后,其脑电信号的奇异谱曲线不存在噪声平台,明显区别于正常人。是否可以认为脑电信号的奇异谱正代表着大脑的一种基本状态,癫痫患者在未发作时,大脑的基本状态已经处于异常。无论如休,奇异系统分析方法使得可以利用很短的一段脑电数据诊断癫痫。无疑为癫痫病人的临床诊断提供了一条简单、有效的途径。     

柴胡对癫痫模型电活动的调制

目的 :研究柴胡对癫痫发作的影响。方法 :以家兔和大鼠为实验对象 ,用毛果芸香碱致痫 ,采用脑电图和细胞外玻璃微电极记录技术 ,观察柴胡对癫痫模型大脑皮层放电及海马脑片场电位的影响。结果 :腹腔注射柴胡后可使癫痫发作次数及发作持续时间显著减少 ,发作间隔时间显著延长 ,(P <0 .0 5 ) ,脑片旁滴注柴胡后使致痫大鼠海马脑片诱发场电位幅度平均降低 2 0 .4 1% ,恢复时间平均为 6 .86min ,(P <0 .0 1)。结论 :柴胡注射液能明显抑制癫痫模型电活动 ,提示柴胡具有抗痫作用     

Application of a dissimilarity index of EEG and its sub-bands on prediction of induced epileptic seizures from rat's EEG signals

ObjectiveEpileptic seizures are defined as manifest of excessive and hyper-synchronous activity of neurons in the cerebral cortex that cause frequent malfunction of the human central nervous system. Therefore, finding precursors and predictors of epileptic seizure is of utmost clinical relevance to reduce the epileptic seizure induced nervous system malfunction consequences. Researchers for this purpose may even guide us to a deep understanding of the seizure generating mechanisms. The goal of this paper is to predict epileptic seizures in epileptic rats.MethodsSeizures were induced in rats using pentylenetetrazole (PTZ) model. EEG signals in interictal, preictal, ictal and postictal periods were then recorded and analyzed to predict epileptic seizures. Epileptic seizures were predicted by calculating an index in consecutive windows of EEG signal and comparing the index with a threshold. In this work, a newly proposed dissimilarity index called Bhattacharyya Based Dissimilarity Index (BBDI), dynamical similarity index and fuzzy similarity index were investigated.ResultsBBDI, dynamical similarity index and fuzzy similarity index were examined on case and control groups and compared to each other. The results show that BBDI outperforms dynamical and fuzzy similarity indices. In order to improve the results, EEG sub-bands were also analyzed. The best result achieved when the proposed dissimilarity index was applied on Delta sub-band that predicts epileptic seizures in all rats with a mean of 299.5 s.ConclusionThe dissimilarity of neural network activity between reference window and present window of EEG signal has a significant increase prior to an epileptic seizure and the proposed dissimilarity index (BBDI) can reveal this variation to predict epileptic seizures. In addition, analyzing EEG sub-bands results in more accurate information about constituent neuronal activities underlying the EEG since certain changes in EEG signal may be amplified when each sub-band is analyzed separately.SignificanceThis paper presents application of a dissimilarity index (BBDI) on EEG signals and its sub-bands to predict PTZ-induced epileptic seizures in rats. Based on the results of this work, BBDI will predict epileptic seizures more accurately and more reliably compared to current indices that increases epileptic patient comfort and improves patient outcomes.     

Abnormal interictal gamma activity may manifest a seizure onset zone in temporal lobe epilepsy

Even though recent studies have suggested that seizures do not occur suddenly and that before a seizure there is a period with an increased probability of seizure occurrence, neurophysiological mechanisms of interictal and pre-seizure states are unknown. The ability of mathematical methods to provide much more sensitive tools for the detection of subtle changes in the electrical activity of the brain gives promise that electrophysiological markers of enhanced seizure susceptibility can be found even during interictal periods when EEG of epilepsy patients often looks 'normal'. Previously, we demonstrated in animals that hippocampal and neocortical gamma-band rhythms (30-100 Hz) intensify long before seizures caused by systemic infusion of kainic acid. Other studies in recent years have also drawn attention to the fast activity (>30 Hz) as a possible marker of epileptogenic tissue. The current study quantified gamma-band activity during interictal periods and seizures in intracranial EEG (iEEG) in 5 patients implanted with subdural grids/intracranial electrodes during their pre-surgical evaluation. In all our patients, we found distinctive (abnormal) bursts of gamma activity with a 3 to 100 fold increase in power at gamma frequencies with respect to selected by clinicians, quiescent, artifact-free, 7-20 min "normal" background (interictal) iEEG epochs 1 to 14 hours prior to seizures. Increases in gamma activity were largest in those channels which later displayed the most intensive electrographic seizure discharges. Moreover, location of gamma-band bursts correlated (with high specificity, 96.4% and sensitivity, 83.8%) with seizure onset zone (SOZ) determined by clinicians. Spatial localization of interictal gamma rhythms within SOZ suggests that the persistent presence of abnormally intensified gamma rhythms in the EEG may be an important tool for focus localization and possibly a determinant of epileptogenesis.     

基于脑电信号的癫痫发作预测特征及识别

解码癫痫发作前脑电信号的神经元集群异常痫样放电活动,对癫痫发作进行有效预测并实施病前干预,可显著减少疾病病损,是癫痫防治的研究热点之一。基于脑电信号的癫痫发作预测研究关键在于发作间期和前期的异常状态识别,研究上述两状态间的神经动力学特征差异对明确癫痫发病机制、选取高分辨特征,进而有效识别该渐进性疾病所处的发作阶段具有重要价值。目前,研究者已对当前主流特征提取及模式识别方法进行了充分的调研梳理,但忽视了神经动态特征变化对于癫痫发作预测的重要意义。基于此,本文归纳总结了5类典型的发作预测特征分析方法及其优缺点,重点剖析了发作间期至前期神经生理特征的动态变化及其动力学特性,类比分析了当前该领域主流的机器学习和深度学习特征识别方法,以期为进一步建立精准、高效的癫痫发作预测技术提供新思路。     

Automatic detection of epileptic EEG signals using higher order cumulant features

The unpredictability of the occurrence of epileptic seizures makes it difficult to detect and treat this condition effectively. An automatic system that characterizes epileptic activities in EEG signals would allow patients or the people near them to take appropriate precautions, would allow clinicians to better manage the condition, and could provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect epileptic activity in EEG recordings. Because of the nonlinear and dynamic nature of EEG signals, the use of nonlinear Higher Order Spectra (HOS) features is a seemingly promising approach. This paper presents the methodology employed to extract HOS features (specifically, cumulants) from normal, interictal, and epileptic EEG segments and to use significant features in classifiers for the detection of these three classes. In this work, 300 sets of EEG data belonging to the three classes were used for feature extraction and classifier development and evaluation. The results show that the HOS based measures have unique ranges for the different classes with high confidence level (p-value < 0.0001). On evaluating several classifiers with the significant features, it was observed that the Support Vector Machine (SVM) presented a high detection accuracy of 98.5% thereby establishing the possibility of effective EEG segment classification using the proposed technique.     

Patient-Specific Detection of Cerebral Blood Flow Alterations as Assessed by Arterial Spin Labeling in Drug-Resistant Epileptic Patients

Electrophysiological and hemodynamic data can be integrated to accurately and precisely identify the generators of abnormal electrical activity in drug-resistant focal epilepsy. Arterial Spin Labeling (ASL), a magnetic resonance imaging (MRI) technique for quantitative noninvasive measurement of cerebral blood flow (CBF), can provide a direct measure of variations in cerebral perfusion associated with the epileptic focus. In this study, we aimed to confirm the ASL diagnostic value in the identification of the epileptogenic zone, as compared to electrical source imaging (ESI) results, and to apply a template-based approach to depict statistically significant CBF alterations. Standard video-electroencephalography (EEG), high-density EEG, and ASL were performed to identify clinical seizure semiology and noninvasively localize the epileptic focus in 12 drug-resistant focal epilepsy patients. The same ASL protocol was applied to a control group of 17 healthy volunteers from which a normal perfusion template was constructed using a mixed-effect approach. CBF maps of each patient were then statistically compared to the reference template to identify perfusion alterations. Significant hypo- and hyperperfused areas were identified in all cases, showing good agreement between ASL and ESI results. Interictal hypoperfusion was observed at the site of the seizure in 10/12 patients and early postictal hyperperfusion in 2/12. The epileptic focus was correctly identified within the surgical resection margins in the 5 patients who underwent lobectomy, all of which had good postsurgical outcomes. The combined use of ESI and ASL can aid in the noninvasive evaluation of drug-resistant epileptic patients.     

Scalp EEG for the diagnosis of epilepsy and photosensitivity in the baboon

Spontaneous seizures have been observed in several baboon species housed at the Southwest National Primate Research Center (SNPRC), including Papio hamadryas anubis and cynocephalus/anubis, hamadryas/anubis, and papio/anubis hybrids. The goal of this study was to establish a noninvasive, reliable electroencephalographic technique to characterize epilepsy phenotypes and assess photosensitivity in these subspecies. Thirty baboons with witnessed seizures, and 15 asymptomatic baboons underwent scalp electroencephalograms (EEGs) with photic stimulation (PS). The sensitivity and specificity of surface EEG for identifying interictal epileptic discharges (IEDs) in baboons with witnessed seizures were examined. The morphology of IEDs, electroclinical features of seizures and responses to PS, reproducibility of EEG findings, and intrarater reliability were also evaluated. Twenty-three seizure baboons (77%) demonstrated IEDs, predominantly with frequencies of 4-6 Hz in 18 baboons and 2-3 Hz in six baboons. Two seizure animals had a mixture of 2-3-Hz and 4-6-Hz IEDs. All animals with 2-3-Hz IEDs were 3 years old or younger. Myoclonic seizures (MS) and generalized tonic-clonic seizures (GTCS) were recorded in 13 baboons (43%). PS activated IEDs in 15 baboons (50%) and seizures in nine baboons. The presence of IEDs or seizures was not associated with a particular gender or species (Fisher exact test, alpha=0.05). Seizures were more common in animals >3 years old, while PS-induced IEDs and seizures were more prevalent in P.h. anubis/cynocephalus crosses compared to P.h. anubis. In the asymptomatic controls, IEDs were recorded in five baboons (33%), and photoparoxysmal responses were observed in two (13%). Surface EEG is a sensitive and reliable instrument for characterizing the epilepsy encountered in Papio species. Electroclinically, the seizure animals had generalized epilepsy with photosensitivity. The variation in IED morphology may be age-related or it may reflect different epileptic phenotypes. Ketamine provoked IEDs and seizures in most seizure animals and only in a few asymptomatic baboons; therefore, it may enhance the sensitivity of surface EEG for detecting a predisposition to epilepsy.     

Electroencephalographic study of the effect of neurotoxin DSP-4 in iron model of chronic focal epilepsy.

The effect of the noradrenergic neurotoxin N-(2-chloroethyl)-N-ethyl-2-bromobenzylamine (DSP-4) on electroencephalographic activity (EEG) was studied in the model of chronic focal epilepsy induced by intracortical injection of FeCl3 in the rat. EEG activity was recorded from the epileptogenic focus (ipsilateral and contralateral) in chronic experiments before and after DSP-4 treatment. In some experiments EEG activity was also simultaneously recorded from the cortical epileptogenic focus and locus coeruleus before and after DSP-4 treatment to study the effect of iron-induced seizure activity and of DSP-4 on the locus coeruleus electrical activity. The results showed that DSP-4 aggravated the iron-induced epileptiform activity as well as the locus-coeruleus electrical activity. The data also showed that, induction of epilepsy by FeCl3 is accompanied by enhancement of the locus coeruleus electrical activity. Our study demonstrates that DSP-4 intensifies and modifies the epileptic activity in the iron-induced chronic epilepsy model and that the effects of toxin persist for a longer duration.     

ICA Extracts Epileptic Sources from fMRI in EEG-Negative Patients: A Retrospective Validation Study

Simultaneous EEG-fMRI has proven to be useful in localizing interictal epileptic activity. However, the applicability of traditional GLM-based analysis is limited as interictal spikes are often not seen on the EEG inside the scanner. Therefore, we aim at extracting epileptic activity purely from the fMRI time series using independent component analysis (ICA). To our knowledge, we show for the first time that ICA can find sources related to epileptic activity in patients where no interictal spikes were recorded in the EEG. The epileptic components were identified retrospectively based on the known localization of the ictal onset zone (IOZ). We demonstrate that the selected components truly correspond to epileptic activity, as sources extracted from patients resemble significantly better the IOZ than sources found in healthy controls. Furthermore, we show that the epileptic components in patients with and without spikes recorded inside the scanner resemble the IOZ in the same degree. We conclude that ICA of fMRI has the potential to extend the applicability of EEG-fMRI for presurgical evaluation in epilepsy.     

Epilepsy and Nonlinear Dynamics

This overview summarizes findings obtained from analyzing electroencephalographic (EEG) recordings from epilepsy patients with methods from the theory of nonlinear dynamical systems. The last two decades have shown that nonlinear time series analysis techniques allow an improved characterization of epileptic brain states and help to gain deeper insights into the spatial and temporal dynamics of the epileptic process. Nonlinear EEG analyses can help to improve the evaluation of patients prior to neurosurgery, and with an unequivocal identification of precursors of seizures, they can be of great value in the development of seizure warning and prevention techniques.     

Complexity of Multi-Channel Electroencephalogram Signal Analysis in Childhood Absence Epilepsy

Absence epilepsy is an important epileptic syndrome in children. Multiscale entropy (MSE), an entropy-based method to measure dynamic complexity at multiple temporal scales, is helpful to disclose the information of brain connectivity. This study investigated the complexity of electroencephalogram (EEG) signals using MSE in children with absence epilepsy. In this research, EEG signals from 19 channels of the entire brain in 21 children aged 5-12 years with absence epilepsy were analyzed. The EEG signals of pre-ictal (before seizure) and ictal states (during seizure) were analyzed by sample entropy (SamEn) and MSE methods. Variations of complexity index (CI), which was calculated from MSE, from the pre-ictal to the ictal states were also analyzed. The entropy values in the pre-ictal state were significantly higher than those in the ictal state. The MSE revealed more differences in analysis compared to the SamEn. The occurrence of absence seizures decreased the CI in all channels. Changes in CI were also significantly greater in the frontal and central parts of the brain, indicating fronto-central cortical involvement of “cortico-thalamo-cortical network” in the occurrence of generalized spike and wave discharges during absence seizures. Moreover, higher sampling frequency was more sensitive in detecting functional changes in the ictal state. There was significantly higher correlation in ictal states in the same patient in different seizures but there were great differences in CI among different patients, indicating that CI changes were consistent in different absence seizures in the same patient but not from patient to patient. This implies that the brain stays in a homogeneous activation state during the absence seizures. In conclusion, MSE analysis is better than SamEn analysis to analyze complexity of EEG, and CI can be used to investigate the functional brain changes during absence seizures.     

一种改进的近似熵——样品熵及其在颞叶癫痫患者脑电信号分析中的应用

采用了近似熵(approximately entropy,ApEn)和它的改进算法,即样品熵(sample entropy,SampEn)分析了8位颞叶癫痫患者和10位健康人员的短程脑电信号。在计算过程中使用了两种滑动窗口和5个不同的过滤标准r。结果显示颞叶癫痫患者组脑电信号的熵值显著低于健康组,而且患者癫痫病灶所在的脑半球的复杂度远远小于非癫痫病灶的脑半球。小的滑动窗口能更多地反映与癫痫发作相关的细节。对于1秒的滑动窗口,过滤标准r不能小于时间序列标准差的0．15％;而对于4秒的滑动窗口,则过滤标准r不能小于时间序列标准差的10％。研究结果表明,在短程脑电信号的非线性分析中,样品熵是一种比近似熵更为可靠的非线性分析方法。颞叶癫痫患者脑电信号的熵值低于健康人员,这可能表明脑电活动的非线性程度的降低是由于神经信号在大脑内的传递受到了阻碍或者损坏,使得神经信号成了相对孤立的信息源。     

A Physiology-Based Seizure Detection System for Multichannel EEG

Background

Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG) signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable.

Methodology

This study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn), statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG) to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching.

Principal Findings

We obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection.

Conclusion

We report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system.     

Analysis of Epileptic Discharges from Implanted Subdural Electrodes in Patients with Sturge-Weber Syndrome

ObjectiveAlmost two-thirds of patients with Sturge-Weber syndrome (SWS) have epilepsy, and half of them require surgery for it. However, it is well known that scalp electroencephalography (EEG) does not demonstrate unequivocal epileptic discharges in patients with SWS. Therefore, we analyzed interictal and ictal discharges from intracranial subdural EEG recordings in patients treated surgically for SWS to elucidate epileptogenicity in this disorder.MethodsFive intractable epileptic patients with SWS who were implanted with subdural electrodes for presurgical evaluation were enrolled in this study. We examined the following seizure parameters: seizure onset zone (SOZ), propagation speed of seizure discharges, and seizure duration by visual inspection. Additionally, power spectrogram analysis on some frequency bands at SOZ was performed from 60 s before the visually detected seizure onset using the EEG Complex Demodulation Method (CDM).ResultsWe obtained 21 seizures from five patients for evaluation, and all seizures initiated from the cortex under the leptomeningeal angioma. Most of the patients presented with motionless staring and respiratory distress as seizure symptoms. The average seizure propagation speed and duration were 3.1 ± 3.6 cm/min and 19.4 ± 33.6 min, respectively. Significant power spectrogram changes at the SOZ were detected at 10–30 Hz from 15 s before seizure onset, and at 30–80 Hz from 5 s before seizure onset.SignificanceIn patients with SWS, seizures initiate from the cortex under the leptomeningeal angioma, and seizure propagation is slow and persists for a longer period. CDM indicated beta to low gamma-ranged seizure discharges starting from shortly before the visually detected seizure onset. Our ECoG findings indicate that ischemia is a principal mechanism underlying ictogenesis and epileptogenesis in SWS.     

Estimating short-run and long-run interaction mechanisms in interictal state

We address the issue of analyzing electroencephalogram (EEG) from seizure patients in order to test, model and determine the statistical properties that distinguish between EEG states (interictal, pre-ictal, ictal) by introducing a new class of time series analysis methods. In the present study: firstly, we employ statistical methods to determine the non-stationary behavior of focal interictal epileptiform series within very short time intervals; secondly, for such intervals that are deemed non-stationary we suggest the concept of Autoregressive Integrated Moving Average (ARIMA) process modelling, well known in time series analysis. We finally address the queries of causal relationships between epileptic states and between brain areas during epileptiform activity. We estimate the interaction between different EEG series (channels) in short time intervals by performing Granger-causality analysis and also estimate such interaction in long time intervals by employing Cointegration analysis, both analysis methods are well-known in econometrics. Here we find: first, that the causal relationship between neuronal assemblies can be identified according to the duration and the direction of their possible mutual influences; second, that although the estimated bidirectional causality in short time intervals yields that the neuronal ensembles positively affect each other, in long time intervals neither of them is affected (increasing amplitudes) from this relationship. Moreover, Cointegration analysis of the EEG series enables us to identify whether there is a causal link from the interictal state to ictal state.