Independent component analysis in the distinction between focal and multifocal spikes.

Independent component analysis (ICA) is a novel method that extracts independent sources in recorded signals. One of its capabilities is to separate epileptiform activity of different origins. The goal of this study was to demonstrate that ICA is useful for differentiating focal and multifocal epilepsies. Using ICA, the authors analyzed 160 samples of patients with unifocal (temporal: 50 samples from 5 patients; frontal: 50 samples from 5 patients) or multifocal epilepsy (bitemporal: 30 samples from 3 patients; multifocal extratemporal: 30 samples from 5 patients). Each sample included at least two spikes. ICA was applied using the JADE algorithm implemented in a Matlab platform. The components were identified visually. The EEG and the isopotential map of the suspected components were reconstructed to demonstrate the nature and location of each spike. In multifocal epilepsies, the spikes were separated in distinct components in all cases. In unifocal temporal epilepsies, ICA extracted all the spikes from the same location into a single component. In the patients with unifocal frontal epilepsy, one component included all the spikes in 80% of the samples; in some of these cases, other components were responsible for small parts of the spikes, but with a close topography to the main component. The waves were separated in different components both in the unifocal and in the multifocal samples. Therefore, in epilepsies with independent foci, ICA separates the spikes of different origins into distinct components. In unifocal epilepsies, ICA tends to locate the spikes from the same area into the same component, especially in temporal epilepsies. The authors conclude that ICA might be a useful tool to distinguish between unifocal and multifocal epilepsies.     

Independent component analysis of ictal EEG in medial temporal lobe epilepsy

PURPOSE: Application of independent component analysis (ICA) to interictal EEGs and to event-related potentials has helped noise reduction and source localization. However, ICA has not been used for the analysis of ictal EEGs in partial seizures. In this study, we applied ICA to the ictal EEGs of patients with medial temporal lobe epilepsy (TLE) and investigated whether ictal components can be separated and whether they indicate correct lateralization. METHODS: Twenty-four EEGs from medial TLE patients were analyzed with the extended ICA algorithm. Among the resultant 20 components in each EEG, we selected components with an ictal nature and reviewed their corresponding topographic maps for the lateralization. We then applied quantitative methods for the verification of increased quality of the reconstructed EEGs. RESULTS: All ictal EEGs were successfully decomposed into one or more ictal components and nonictal components. After EEG reconstruction with exclusion of artifacts, the lateralizing power of the ictal EEG was increased from 75 to 96%. CONCLUSIONS: ICA can separate successfully the manifold components of ictal rhythms and can improve EEG quality.     

Independent component analysis as a tool to eliminate artifacts in EEG: a quantitative study.

Independent component analysis (ICA) is a novel technique that calculates independent components from mixed signals. A hypothetical clinical application is to remove artifacts in EEG. The goal of this study was to apply ICA to standard EEG recordings to eliminate well-known artifacts, thus quantifying its efficacy in an objective way. Eighty samples of recordings with spikes and evident artifacts of electrocardiogram (EKG), eye movements, 50-Hz interference, muscle, or electrode artifact were studied. ICA components were calculated using the Joint Approximate Diagonalization of Eigen-matrices (JADE) algorithm. The signal was reconstructed excluding those components related to the artifacts. A normalized correlation coefficient was used as a measure of the changes caused by the suppression of these components. ICA produced an evident clearing-up of signals in all the samples. The morphology and the topography of the spike were very similar before and after the removal of the artifacts. The correlation coefficient showed that the rest of the signal did not change significantly. Two examiners independently looked at the samples to identify the changes in the morphology and location of the discharge and the artifacts. In conclusion, ICA proved to be a useful tool to clean artifacts in short EEG samples, without having the disadvantages associated with the digital filters. The distortion of the interictal activity measured by correlation analysis was minimal.     

Quantitative analysis of EEG background activity in patients with rolandic spikes.

OBJECTIVE: The aim was to compare the background activity, through quantitative EEG analysis, of patients with rolandic spikes and normal age-matched controls. MATERIAL AND METHODS: Twenty-one channel EEG of 23 children with rolandic spikes and 39 normal children, with ages ranging from 7 to 12 years, were submitted to quantitative analysis (FFT) of discharge-free epochs. Patients and controls were divided in groups according to age (7-9 and 10-12 years old). Delta, theta, alpha and beta frequency ranges were compared between groups for all electrode positions. RESULTS: Comparing normals, the 7-9 years old group showed power reduction in the alpha and beta ranges. Comparing patients and normal age-matched groups, the patients showed power increase, at all frequency ranges in the 7-9 years old group and at delta and theta frequency ranges in the 10-12 years old group. CONCLUSIONS: Our findings agree with recent evidences that these children may differ from normal (besides the eventual occurrence of seizures); but they also suggest that these differences can be related to immaturity and not necessarily related to the epileptiform discharge.     

Independent component approach to the analysis of EEG recordings at early stages of depressive disorders

ObjectiveA modern approach for blind source separation of electrical activity represented by Independent Components Analysis (ICA) was used for QEEG analysis in depression.MethodsThe spectral characteristics of the resting EEG in 111 adults in the early stages of depression and 526 non-depressed subjects were compared between groups of patients and healthy controls using a combination of ICA and sLORETA methods.ResultsComparison of the power of independent components in depressed patients and healthy controls have revealed significant differences between groups for three frequency bands: theta (4–7.5 Hz), alpha (7.5–14 Hz), and beta (14–20 Hz) both in Eyes closed and Eyes open conditions. An increase in slow (theta and alpha) activity in depressed patients at parietal and occipital sites may reflect a decreased cortical activation in these brain regions, and a diffuse enhancement of beta power may correlate with anxiety symptoms playing an important role on the onset of depressive disorder.ConclusionsICA approach used in the present study allowed us to localize the EEG spectra differences between the two groups.SignificanceA relatively rare approach which uses the ICA spectra for comparison of the quantitative parameters of EEG in different groups of patients/subjects allows to improve an accuracy of measurement.     

Independent component analysis in the study of focal seizures.

Independent component analysis (ICA) is a novel technique that can separate statistically independent elements from complex signals. It has demonstrated its utility in separating artifacts and analyzing interictal discharges in EEG. ICA has been used recently in ictal recordings, showing the possibility of isolating the ictal activity. The goal of our study was to analyze focal seizures with ICA, decomposing the elements of the seizures to understand their genesis and propagation, and to differentiate between various types of focal seizures. We studied 26 focal seizures of temporal, frontal, or parietal origin. Only seizures with suspected focal onset were included in the study. The EEG recordings were acquired by using standard video-EEG equipment, with scalp electrodes. All the off-line analysis was carried out on a PC by means of specific software developed in the Matlab environment. ICA components were calculated with the use of the JADE (Joint Approximate Diagonalization of Eigen-matrices) algorithm. The decomposition of the seizures varied according to the EEG seizure pattern. In the seizures with focal rhythmic theta slow or sharp waves, the rhythmic activity was separated into one to five components, having an initial component with a clear concordance with the focus, whereas the others had an onset a few milliseconds later and corresponded to neighboring areas. In the 6 frontal seizures with regional rhythmic low voltage fast activity, 4 to 10 components were found, practically with a simultaneous timing, having a frontal distribution. In the three frontal seizures with a diffuse attenuation of the EEG signal, it was not possible to differentiate components of cerebral origin from the components of muscle artifact. ICA is an interesting tool to study the nature of focal seizures. The results depend on the EEG pattern. In the seizures with a clear EEG focal pattern, ICA may be useful to separate components of the ictal onset from the propagated activity.     

Independent component analysis: algorithms and applications.

A fundamental problem in neural network research, as well as in many other disciplines, is finding a suitable representation of multivariate data, i.e. random vectors. For reasons of computational and conceptual simplicity, the representation is often sought as a linear transformation of the original data. In other words, each component of the representation is a linear combination of the original variables. Well-known linear transformation methods include principal component analysis, factor analysis, and projection pursuit. Independent component analysis (ICA) is a recently developed method in which the goal is to find a linear representation of non-Gaussian data so that the components are statistically independent, or as independent as possible. Such a representation seems to capture the essential structure of the data in many applications, including feature extraction and signal separation. In this paper, we present the basic theory and applications of ICA, and our recent work on the subject.     

Intracranial EEG substrates of scalp EEG interictal spikes

PURPOSE: To determine the area of cortical generators of scalp EEG interictal spikes, such as those in the temporal lobe epilepsy. METHODS: We recorded simultaneously 26 channels of scalp EEG with subtemporal supplementary electrodes and 46 to 98 channels of intracranial EEG in 16 surgery candidates with temporal lobe epilepsy. Cerebral discharges with and without scalp EEG correlates were identified, and the area of cortical sources was estimated from the number of electrode contacts demonstrating concurrent depolarization. RESULTS: We reviewed approximately 600 interictal spikes recorded with intracranial EEG. Only a very few of these cortical spikes were associated with scalp recognizable potentials; 90% of cortical spikes with a source area of >10 cm(2) produced scalp EEG spikes, whereas only 10% of cortical spikes having <10 cm(2) of source area produced scalp potentials. Intracranial spikes with <6 cm(2) of area were never associated with scalp EEG spikes. CONCLUSIONS: Cerebral sources of scalp EEG spikes are larger than commonly thought. Synchronous or at least temporally overlapping activation of 10-20 cm(2) of gyral cortex is common. The attenuating property of the skull may actually serve a useful role in filtering out all but the most significant interictal discharges that can recruit substantial surrounding cortex.     

Significance of centro-temporal spikes on the EEG

In a prospectively studied group of 43 children with centro-temporal spikes on the EEG, only 26 had the classical epileptic syndrome related to this EEG abnormality. Ten patients never had epileptic manifestations. In the other seven cases two types of epileptiform activity were encountered: in addition to the centro-temporal spikes generalized spike-and-wave complexes or - in other patients - occipital epileptiform phenomena occurred. In these cases the clinical signs and symptoms correlated best with the non-Rolandic EEG abnormalities. Some patients had only a few centro-temporal spikes during the recording of the EEG; in others this epileptiform activity was almost continuously present. This quantitative difference had no clinical correlation. Centro-temporal spikes should probably be looked upon as an (epi)phenomenon of cerebral disfunction with a wide range of clinical and electroencephalographic expressions.     

Independent component analysis for artefact separation in astrophysical images.

In this paper, we demonstrate that independent component analysis, a novel signal processing technique, is a powerful method for separating artefacts from astrophysical image data.When studying far-out galaxies from a series of consequent telescope images, there are several sources for artefacts that influence all the images, such as camera noise, atmospheric fluctuations and disturbances, cosmic rays, and stars in our own galaxy. In the analysis of astrophysical image data it is very important to implement techniques which are able to detect them with great accuracy, to avoid the possible physical events from being eliminated from the data along with the artefacts.For this problem, the linear ICA model holds very accurately because such artefacts are all theoretically independent of each other and of the physical events. Using image data on the M31 Galaxy, it is shown that several artefacts can be detected and recognized based on their temporal pixel luminosity profiles and independent component images. The obtained separation is good and the method is very fast. It is also shown that ICA outperforms principal component analysis in this task. For these reasons, ICA might provide a very useful pre-processing technique for the large amounts of available telescope image data.     

Independent component analysis as a model-free approach for the detection of BOLD changes related to epileptic spikes: a simulation study

EEG-fMRI in epileptic patients is commonly analyzed using the general linear model (GLM), which assumes a known hemodynamic response function (HRF) to epileptic spikes in the EEG. In contrast, independent component analysis (ICA) can extract Blood-Oxygenation Level Dependent (BOLD) responses without imposing constraints on the HRF. This technique was evaluated on data generated by superimposing artificial responses on real background fMRI signals. Simulations were run using a wide range of EEG spiking rates, HRF amplitudes, and activation regions. The data were decomposed by spatial ICA into independent components. A deconvolution method then identified component time courses significantly related to the simulated spikes, without constraining the shape of the HRF. Components matching the simulated activation regions ("concordant components") were found in 84.4% of simulations, while components at discordant locations were found in 12.2% of simulations. These false activations were often related to large artifacts that coincidentally occurred simultaneously with some of the random simulated spikes. The performance of the method depended closely on the simulation parameters; when the number of spikes was low, concordant components could only be identified when HRF amplitudes were large. Although ICA did not depend on the shape of the HRF, data processed with the GLM did not reveal the appropriate activation region when the HRF varied slightly from the canonical shape used in the model. ICA may thus be able to extract BOLD responses from EEG-fMRI data in epileptic patients, in a way that is robust to uncertainty and variability in the shape of the HRF.     

Independent component analysis at the neural cocktail party

'Independent component analysis' is a technique of data transformation that finds independent sources of activity in recorded mixtures of sources. It can be used to recover fluctuations of membrane potential from individual neurons in multiple-detector optical recordings. There are some examples in which more than 100 neurons can be separated simultaneously. Independent component analysis automatically separates overlapping action potentials, recovers action potentials of different sizes from the same neuron, removes artifacts and finds the position of each neuron on the detector array. One limitation is that the number of sources--neurons and artifacts--must be equal to or less than the number of simultaneous recordings. Independent component analysis also has many other applications in neuroscience including, removal of artifacts from EEG data, identification of spatially independent brain regions in fMRI recordings and determination of population codes in multi-unit recordings.     

High-resolution EEG: cortical potential imaging of interictal spikes.

BACKGROUND: It is of clinical importance to localize pathologic brain tissue in epilepsy. Noninvasive localization of cortical areas associated with interictal epileptiform spikes may provide important information to facilitate presurgical planning for intractable epilepsy patients. METHODS: A cortical potential imaging (CPI) technique was used to deconvolve the smeared scalp potentials into the cortical potentials. A 3-spheres inhomogeneous head model was used to approximately represent the head volume conductor. Five pediatric epilepsy patients were studied. The estimated cortical potential distributions of interictal spikes were compared with the subsequent surgical resections of these same patients. RESULTS: The areas of negativity in the reconstructed cortical potentials of interictal spikes in 5 patients were consistent with the areas of surgical resections for these patients. CONCLUSIONS: The CPI technique may become a useful alternative for noninvasive mapping of cortical regions displaying epileptiform activity from scalp electroencephalogram recordings.     

Spontaneous EEG spikes in the normal hippocampus. III. Relations to evoked potentials

Spontaneous EEG spikes (SPKs) were recorded from the CA1 region of the dorsal hippocampus in normal rats during behavioral states not accompanied by rhythmical slow activity (RSA). SPKs were positive in stratum oriens, negative in stratum radiatum and accompanied by population bursts (PBs) in stratum pyramidale. In order to examine the origin of SPKs and PBs single pulse or brief high frequency electrical stimuli were applied to the Schaffer collateral/commissural pathway. Evoked potentials were recorded and compared with spontaneous SPKs and PBs. The results indicate the following: (1) the laminar amplitude profile of spontaneous SPKs was similar to that of population EPSPs evoked by stimulation of the Schaffer collateral/commissural pathway; (2) the population EPSP most similar to the spontaneous SPK was evoked by a brief (20-60 msec) train of high frequency (125-500 Hz) pulses; (3) the same pattern of stimulation was also found to be most efficient in evoking a series of multiple population spikes resembling a type of spontaneous PB (ripple). These observations suggest that SPKs and PBs in CA1 represent population EPSPs and multiple population spikes, respectively and that these CA1 events are triggered by brief, high frequency burst discharges of CA3 pyramidal cells via the Schaffer collateral and commissural pathway.     

Independent component analysis removing artifacts in ictal recordings

PURPOSE: Independent component analysis (ICA) is a novel algorithm able to separate independent components from complex signals. Studies in interictal EEG demonstrate its usefulness to eliminate eye, muscle, 50-Hz, electrocardiogram (ECG), and electrode artifacts. The goal of this study was to evaluate the usefulness of ICA in removing artifacts in ictal recordings with a known EEG onset. METHODS: We studied 20 seizures of nine patients with focal epilepsy monitored in our video-EEG monitoring unit. ICA was applied to remove obvious artifacts in segments at the beginning of the seizure. The final EEGs were exported to the original format and were compared with the original EEG by two blinded examiners. We compared original recordings and the samples cleaned by digital filters (DFs), ICA and ICA plus digital filters (ICA + DFs), evaluating the possibility of finding an ictal pattern, the localization of the onset in area and time, and the global quality of the sample. RESULTS: All the recordings except one (95%) improved after the use of ICA for the elimination of blinking and other artifacts. Three seizures were found in which in the original recordings did not permit us to detect an ictal pattern, and after ICA + DFs, an ictal onset was evident; in two of them, ICA alone was able to show this pattern. The best results in all the scores were obtained with ICA + DF. ICA was better than DFs. The agreement between the two reviewers was highly significant. CONCLUSIONS: ICA is useful to remove artifacts from ictal recordings. When applied to ictal recordings, it increases the quality of the recording. In some cases, ICA may be useful to show ictal onsets obscured by artifacts. ICA + DFs obtained the best results regarding removal of the artifacts.     

Two types of EEG spikes in the kindled rabbit hippocampus

The characteristics of interictal EEG spikes were studied in the rabbit hippocampus. After kindling for 2-4 weeks, rabbits were anesthetized and curarized for acute experiments. There were two types of EEG spikes in the kindled hippocampus; one (B) had its source in the stimulated side, and the other (A) in the contralateral side. In addition, compound EEG spikes (C) were observed that consisted basically of B and A spikes. Intracellular counterparts of A and B spikes were generally a depolarization-hyperpolarization sequence in the CA1 pyramidal cells. On the other hand, counterparts of the C spikes were initial depolarization with a superposed spike burst followed by relatively shorter lasting hyperpolarization which seemed to indicate an enhancement of excitation during the kindling process.     

Focal magnetoencephalographic spikes in the superior temporal plane undetected by scalp EEG.

Temporal lobe spikes were detected by magnetoencephalography (MEG), but not by standard scalp electroencephalography (EEG), in a patient with intractable complex partial seizures. Simultaneous recording of scalp EEG and MEG revealed 2 different types of spike discharges: sporadic single spikes detected by both EEG and MEG which were localised diffusely in the right temporal lobe; and rhythmic MEG spike discharges that were not detected by scalp EEG, focally localised in the posterior part of the superior temporal plane. The tangential current orientation to the scalp may explain the different sensitivity of scalp EEG and MEG to rhythmic discharges. This study shows the unique sensitivity of MEG to epileptic activity in the superior temporal plane.     

基于独立成分分析方法的海洛因成瘾者功能磁共振成像研究

目的 使用独立成分分析(ICA)法分析功能磁共振(fMRI)数据研究线索诱导因素导致海洛因成瘾者复吸的神经网络. 方法 安徽医科大学附属省立医院神经外科自2010年2月至2010年12月对海洛因成瘾组(n=15,来自安徽省戒毒康复中心)和正常对照组(n=15,来自招募的志愿者)在接受吸毒相关场景视频刺激的同时进行fMRI扫描,然后使用ICA方法对数据进行分析和比较2组受试者脑区的激活情况. 结果 与正常对照组相比,海洛因成瘾组患者双侧前额叶、左前扣带回、双侧后扣带回、左侧顶叶、左侧颞下回的激活降低;双侧伏核、右侧海马以及部分枕叶的激活增强,差异有统计学意义(P＜0.05). 结论 ICA方法是一种有效的任务相关fMRI数据分析方法,海洛因成瘾者学习记忆和奖赏系统相关脑区功能改变是线索诱发复吸的重要原因,主要涉及左前额叶、左顶叶、前后扣带回、海马、伏核等脑区.     

Ictal EEG patterns in epilepsy with centro-temporal spikes

Purpose: To describe the EEG pattern of seizures in patients with benign childhood epilepsy with centro-temporal spikes (BCECTS). Methods: The clinical and EEG data of 701 BCECTS patients with at least a 3 years follow-up were reviewed from 10 epilepsy centers. Results: Thirty-four seizures were recorded in 30 patients. Four different ictal EEG patterns (A-D) were identified. The most frequent (pattern A) was characterized by low voltage activity of fast rhythmic spikes, increasing in amplitude and decreasing in frequency, and occurred in 14 children. Pattern B (six patients) was constituted by a discharge of spikes intermixed with sharp waves increasing in frequency and amplitude. Pattern C (seven children) consisted of monomorphic theta which progressively formed a discharge increasing in amplitude and decreasing in frequency. Pattern D (5 children) was characterized by a initial focal depression of the electrical activity, followed by one of the three above described patterns. In 21 out of 28 children, the initial ictal pattern, altered from one pattern to another one. No clinical or EEG feature was predictive of a specific ictal pattern. Discussion: We failed to identify a unique ictal EEG pattern in our patients with BCECTS. The occurrence of per-ictal features, e.g., initial EEG depression or post-ictal slowing, is common and should not be interpreted with prejudice. Alteration of ictal EEG pattern from one to another is not in conflict with the diagnosis of BCECTS.