Ing. Petr Ježdík, Ph.D.

Antagonistic activity of brain networks likely plays a fundamental role in how the brain optimizes its performance by efficient allocation of computational resources. A prominent example involves externally/internally oriented attention tasks, implicating two anticorrelated, intrinsic brain networks: the default mode network (DMN) and the dorsal attention network (DAN). To elucidate electrophysiological underpinnings and causal interplay during attention switching, we recorded intracranial EEG (iEEG) from 25 epilepsy patients with electrode contacts localized in the DMN and DAN. We show antagonistic network dynamics of activation-related changes in high-frequency (> 50 Hz) and low-frequency (< 30 Hz) power. The temporal profile of information flow between the networks estimated by functional connectivity suggests that the activated network inhibits the other one, gating its activity by increasing the amplitude of the low-frequency oscillations. Insights about inter-network communication may have profound implications for various brain disorders in which these dynamics are compromised.

Objective: Epilepsy surgery in the operculo-insular cortex is challenging due to the difficult delineation of the epileptogenic zone and the high risk of post-operative deficits following resections in this region. Methods: Pre- and post-surgical data from 30 pediatric patients who underwent opercular-insular cortex surgery at Motol Epilepsy Center Prague from 2010 to 2022 were analyzed. Results: Focal cortical dysplasia (FCD, n = 15, 50%) was the predominant cause of epilepsy in the patients studied, followed by epilepsy-associated tumors (n = 5, 17%) and tuberous sclerosis complex (n = 2, 7%). In eight patients where FCD was the most likely etiology, histology was negative. Seven patients (23%) displayed normal MRI results. Seizures exhibited diverse semiology and propagation patterns (i.e., frontal, perisylvian, and temporal). Ictal and interictal EEG findings observed during video EEG were mostly extensive. Multimodal imaging and advanced post-processing were frequently utilized. Stereoelectroencephalography (SEEG) monitoring was used for localizing the epileptogenic zone and eloquent cortex in 23 patients (77%). Oblique electrodes were used as guides for better orientation of neurosurgeons during resection. The epileptogenic zone was in the dominant hemisphere in 16 patients. At the two-year follow-up, 22 patients (73%) were completely seizure-free, and eight (27%) experienced a seizure frequency reduction of > 50% (ILAE 3 and 4). Fourteen patients (47%) underwent anti-seizure medication tapering; treatment was completely withdrawn in two patients (7%). Nineteen patients (63%) remained seizure-free following the definitive outcome assessment (median: 6 years and 5 months, range: 2 years to 13 years and 5 months post-surgery). Six patients (20 %) experienced corona radiata or basal ganglia ischemia; four (13%) improved to mild hemiparesis, and one (3%) to moderate hemiparesis with ongoing rehabilitation. Two patients (7 %) operated on in the anterior insula, along with fronto-temporal resection experienced major complications - pontine ischemia and post-operative brain edema. Significance: Epilepsy surgery in the opercular-insular cortex can lead to excellent patient outcomes. A comprehensive diagnostic approach is crucial for surgical success. There is a great chance for significant recovery of postoperative deficits with the use of rehabilitation.

Visuospatial perspective-taking (VPT) is the ability to imagine a scene from a position different from the one used in self-perspective judgments (SPJ). We typically use VPT to understand how others see the environment. VPT requires overcoming the self-perspective, and impairments in this process are implicated in various brain disorders, such as schizophrenia and autism. However, the underlying brain areas of VPT are not well distinguished from SPJ-related ones and from domain-general responses to both perspectives. In addition, hierarchical processing theory suggests that domain-specific processes emerge over time from domain-general ones. It mainly focuses on the sensory system, but outside of it, support for this hypothesis is lacking. Therefore, we aimed to spatiotemporally distinguish brain responses domain-specific to VPT from the specific ones to self-perspective, and domain-general responses to both perspectives. In particular, we intended to test whether VPT- and SPJ specific responses begin later than the general ones. We recorded intracranial EEG data from 30 patients with epilepsy who performed a task requiring laterality judgments during VPT and SPJ, and analyzed the spatiotemporal features of responses in the broad gamma band (50–150 Hz). We found VPT-specific processing in a more extensive brain network than SPJ-specific processing. Their dynamics were similar, but both differed from the general responses, which began earlier and lasted longer. Our results anatomically distinguish VPT-specific from SPJ-specific processing. Furthermore, we temporally differentiate between domain-specific and domain-general processes both inside and outside the sensory system, which serves as a novel example of hierarchical processing.

Current advances in epilepsy treatment aim to personalize and responsively adjust treatment parameters to overcome patient heterogeneity in treatment efficiency. For tailoring treatment to the individual and the current brain state, tools are required that help to identify the patient- and time-point-specific parameters of epilepsy. Computational modeling has long proven its utility in gaining mechanistic insight. Recently, the technique has been introduced as a diagnostic tool to predict individual treatment outcomes. In this article, the Wendling model, an established computational model of epilepsy dynamics, is used to automatically classify epileptic brain states in intracranial EEG from patients (n = 4) and local field potential recordings from in vitro rat data (high-potassium model of epilepsy, n = 3). Five-second signal segments are classified to four types of brain state in epilepsy (interictal, preonset, onset, ictal) by comparing a vector of signal features for each data segment to four prototypical feature vectors obtained by Wendling model simulations. The classification result is validated against expert visual assessment. Model-driven brain state classification achieved a classification performance significantly above chance level (mean sensitivity 0.99 on model data, 0.77 on rat data, 0.56 on human data in a four-way classification task). Model-driven prototypes showed similarity with data-driven prototypes, which we obtained from real data for rats and humans. Our results indicate similar electrophysiological patterns of epileptic states in the human brain and the animal model that are well-reproduced by the computational model, and captured by a key set of signal features, enabling fully automated and unsupervised brain state classification in epilepsy.

Objective: Focal cortical dysplasia (FCD) is the most common malformation causing refractory focal epilepsy. Surgical removal of the entire dysplastic cortex is crucial for achieving a seizure-free outcome. Precise presurgical distinctions between FCD types by neuroimaging are difficult, mainly in patients with normal magnetic resonance imaging findings. However, the FCD type is important for planning the extent of surgical approach and counselling. Methods: This study included patients with focal drug-resistant epilepsy and definite histopathological FCD type I or II diagnoses who underwent intracranial electroencephalography (iEEG). We detected interictal epileptiform discharges (IEDs) and their recruitment into repetitive discharges (RDs) to compare electrophysiological patterns characterizing FCD types. Results: Patients with FCD type II had a significantly higher IED rate (p<0.005), a shorter inter-discharge interval within RD episodes (p<0.003), sleep influence on decreased RD periodicity (p<0.036), and longer RD episode duration (p<0.003) than patients with type I. A Bayesian classifier stratified FCD types with 82% accuracy. Conclusion: Temporal characteristics of IEDs and RDs reflect the histological findings of FCD subtypes and can differentiate FCD types I and II. Significance: Presurgical prediction of FCD type can help to plan a more tailored surgical approach in patients with normal magnetic resonance findings.

Stereoelectroencephalography (SEEG) records electrical brain activity with intracerebral electrodes. However, it has an inherently limited spatial coverage. Electrical source imaging (ESI) infers the position of the neural generators from the recorded electric potentials, and thus, could overcome this spatial undersampling problem. Here, we aimed to quantify the accuracy of SEEG ESI under clinical conditions. We measured the somatosensory evoked potential (SEP) in SEEG and in high-density EEG (HD-EEG) in 20 epilepsy surgery patients. To localize the source of the SEP, we employed standardized low resolution brain electromagnetic tomography (sLORETA) and equivalent current dipole (ECD) algorithms. Both sLORETA and ECD converged to similar solutions. Reflecting the large differences in the SEEG implantations, the localization error also varied in a wide range from 0.4 to 10 cm. The SEEG ESI localization error was linearly correlated with the distance from the putative neural source to the most activated contact. We show that it is possible to obtain reliable source reconstructions from SEEG under realistic clinical conditions, provided that the high signal fidelity recording contacts are sufficiently close to the source of the brain activity. SEEG-based ESI is able to improve the localization of epileptogenic and functional networks, beyond the vicinity of the intracerebral recording electrodes.

Objective: Although genetic causes of drug-resistant focal epilepsy and selected focal malformations of cortical development (MCD) have been described, a limited number of studies comprehensively analysed genetic diagnoses in patients undergoing pre-surgical evaluation, their outcomes and the effect of genetic diagnosis on surgical strategy. Methods: We analysed a prospective cohort of children enrolled in epilepsy surgery program over January 2018-July 2022. The majority of patients underwent germline and/or somatic genetic testing. We searched for predictors of surgical outcome and positive result of germline genetic testing. Results: Ninety-five patients were enrolled in epilepsy surgery program and 64 underwent resective epilepsy surgery. We ascertained germline genetic diagnosis in 13/74 patients having underwent germline gene testing (pathogenic or likely pathogenic variants in CHRNA4, NPRL3, DEPDC5, FGF12, GRIA2, SZT2, STXBP1) and identified three copy number variants. Thirty-five patients underwent somatic gene testing; we detected 10 pathogenic or likely pathogenic variants in genes SLC35A2, PTEN, MTOR, DEPDC5, NPRL3. Germline genetic diagnosis was significantly associated with the diagnosis of focal epilepsy with unknown seizure onset. Significance: Germline and somatic gene testing can ascertain a definite genetic diagnosis in a significant subgroup of patients in epilepsy surgery programs. Diagnosis of focal genetic epilepsy may tip the scales against the

Magnetic Resonance Imaging (MRI) has revolutionized our ability to non-invasively study the brain's structural and functional properties. However, detecting myelin, a crucial component of white matter, remains challenging due to its indirect visibility on conventional MRI scans. Myelin plays a vital role in neural signal transmission and is associated with various neurological conditions. Understanding myelin distribution and content is crucial for insights into brain development, aging, and neurological disorders. Although specialized MRI sequences can estimate myelin content, these are time-consuming. Also, many patients sent to specialized neurological centers have an MRI of the brain already scanned. In this study, we focused on techniques utilizing standard MRI T1-weighted (T1w) and T2-weighted (T2w) sequences commonly used in brain imaging protocols. We evaluated the applicability of the T1w/T2w ratio in assessing myelin content by comparing it to quantitative T1 mapping (qT1). Our study included 1 healthy adult control and 7 neurologic patients (comprising both pediatric and adult populations) with epilepsy originating from focal epileptogenic lesions visible on MRI structural scans. Following image acquisition on a 3T Siemens Vida scanner, datasets were co-registered, and segmented into anatomical regions using the Fastsurfer toolbox, and T1w/T2w ratio maps were calculated in Matlab software. We further assessed interhemispheric differences in volumes of individual structures, their signal intensity, and the correlation of the T1w/T2w ratio to qT1. Our data demonstrate that in situations where a dedicated myelin-sensing sequence such as qT1 is not available, the T1w/T2w ratio provides significantly better information than T1w alone. By providing indirect information about myelin content, this technique offers a valuable tool for understanding the neurobiology of myelin-related conditions using basic brain scans.

Spatial reference frames (RFs) play a key role in spatial cognition, especially in perception, spatial memory, and navigation. There are two main types of RFs: egocentric (self-centered) and allocentric (object-centered). Although many fMRI studies examined the neural correlates of egocentric and allocentric RFs, they could not sample the fast temporal dynamics of the underlying cognitive processes. Therefore, the interaction and timing between these two RFs remain unclear. Taking advantage of the high temporal resolution of intracranial EEG (iEEG), we aimed to determine the timing of egocentric and allocentric information processing and describe the brain areas involved. We recorded iEEG and analyzed broad gamma activity (50–150 Hz) in 37 epilepsy patients performing a spatial judgment task in a three-dimensional circular virtual arena. We found overlapping activation for egocentric and allocentric RFs in many brain regions, with several additional egocentric- and allocentric-selective areas. In contrast to the egocentric responses, the allocentric responses peaked later than the control ones in frontal regions with overlapping selectivity. Also, across several egocentric or allocentric selective areas, the egocentric selectivity appeared earlier than the allocentric one. We identified the maximum number of egocentric-selective channels in the medial occipito-temporal region and allocentric-selective channels around the intraparietal sulcus in the parietal cortex. Our findings favor the hypothesis that egocentric spatial coding is a more primary process, and allocentric representations may be derived from egocentric ones. They also broaden the dominant view of the dorsal and ventral streams supporting egocentric and allocentric space coding, respectively.

Background and Objectives Malformations of cortical development (MCD), though individually rare, constitute a significant burden of disease. The diagnostic yield of next-generation sequencing (NGS) in these patients varies across studies and methods, and novel genes and variants continue to emerge. Methods Patients (n = 123) with a definite radiologic or histopathologic diagnosis of MCD, with or without epilepsy were included in this study. They underwent NGS-based targeted gene panel (TGP) testing, whole-exome sequencing (WES), or WES-based virtual panel testing. Selected patients who underwent epilepsy surgery (n = 69) also had somatic gene testing of brain tissue–derived DNA. We analyzed predictors of positive germline genetic finding and diagnostic yield of respective methods. Results Pathogenic or likely pathogenic germline genetic variants were detected in 21% of patients (26/123). In the surgical subgroup (69/123), we performed somatic sequencing in 40% of cases (28/69) and detected causal variants in 18% (5/28). Diagnostic yield did not differ between TGP, WES-based virtual gene panel, and open WES (p = 0.69). Diagnosis of focal cortical dysplasia type 2A, epilepsy, and intellectual disability were associated with positive results of germline testing. We report previously unpublished variants in 16/26 patients and 4 cases of MCD with likely pathogenic variants in non-MCD genes. Discussion In this study, we are reporting genetic findings of a large cohort of MCD patients with epilepsy or potentially epileptogenic MCD. We determine predictors of successful ascertainment of a genetic diagnosis in real-life setting and report novel, likely pathogenic variants in MCD and non-MCD genes alike.

Objective Epilepsy surgery fails in >30% of patients with focal cortical dysplasia (FCD). The seizure persistence after surgery can be attributed to the inability to precisely localize the tissue with an endogenous potential to generate seizures. In this study, we aimed to identify the critical components of the epileptic network that were actively involved in seizure genesis. Methods The directed transfer function was applied to intracranial EEG recordings and the effective connectivity was determined with a high temporal and frequency resolution. Pre-ictal network properties were compared with ictal epochs to identify regions actively generating ictal activity and discriminate them from the areas of propagation. Results Analysis of 276 seizures from 30 patients revealed the existence of a seizure-related network reconfiguration in the gamma-band (25-170 Hz; p<0.005) – ictogenic nodes. Unlike seizure onset zone, resecting the majority of ictogenic nodes correlated with favorable outcomes (p<0.012). Conclusion The prerequisite to successful epilepsy surgery is the accurate identification of brain areas from which seizures arise. We show that in FCD-related epilepsy, gamma-band network markers can reliably identify and distinguish ictogenic areas in macroelectrode recordings, improve intracranial EEG interpretation and better delineate the epileptogenic zone. Significance Ictogenic nodes localize the critical parts of the epileptogenic tissue and increase the diagnostic yield of intracranial evaluation.

Object Epilepsy surgery is an effective treatment for selected patients with focal intractable epilepsy. Complete removal of the epileptogenic zone significantly increases the chances for postoperative seizure-freedom. In complex surgical candidates, delineation of the epileptogenic zone requires a long-term invasive video/EEG from intracranial electrodes. It is especially challenging to achieve a complete resection in deep brain structures such as opercular-insular cortex. We report a novel approach utilizing intraoperative visual detection of stereotactically implanted depth electrodes to inform and guide the extent of surgical resection. Methods We retrospectively reviewed data of pediatric patients operated in Motol Epilepsy Center between October 2010 and June 2020 who underwent resections guided by intraoperative visual detection of depth electrodes following SEEG. The outcome in terms of seizure- and AED-freedom was assessed individually in each patient. Results Nineteen patients (age at surgery 2.9–18.6 years, median 13 years) were included in the study. The epileptogenic zone involved opercular-insular cortex in eighteen patients. The intraoperative detection of the electrodes was successful in seventeen patients and the surgery was regarded complete in sixteen. Thirteen patients were seizure-free at final follow-up including six drug-free cases. The successful intraoperative detection of the electrodes was associated with favorable outcome in terms of achieving complete resection and seizure-freedom in most cases. On the contrary, the patients in whom the procedure failed had poor postsurgical outcome. Conclusion The reported technique helps to achieve the complete resection in challenging patients with the epileptogenic zone in deep brain structures.

Abstract Objective: Patients with tuberous sclerosis complex (TSC) present with drug- resistant epilepsy in about 60% of cases, and evaluation for epilepsy surgery may be warranted. Correct delineation of the epileptogenic zone (EZ) among multiple dysplastic lesions on MRI represents a challenging step in pre- surgical evaluation. Methods: Two experienced neuroradiologists evaluated pre- and post- surgical MRIs of 28 epilepsy surgery patients with TSC, assessing characteristics of tubers, cysts, calcifications, and focal cortical dysplasia (FCD)— resembling lesions. Utilizing multiple metrics, we compared MRI features of the EZ— defined as the resected area in TSC patients who achieved seizure- freedom 2 years after epilepsy surgery— with features of other brain areas. Using combinatorial analysis, we identified combinations of dysplastic features that are most frequently observed in the epileptogenic zone in TSC patients. Results: All TSC- associated dysplastic features were more frequently observed in the EZ than in other brain areas (increased cortical thickness, gray- white matter blurring, transmantle sign, calcifications, and tubers; Kendal's tau 0.35, 0.25, 0.27, 0.26, and 0.23, respectively; P value <.001 in all). No single feature could reliably and independently indicate the EZ in all patients. Conversely, the EZ was indicated by the presence of the combination of three of the following features: tubers, transmantle sign, increased cortical thickness, calcifications, and the largest FCD- affected area. Out of these, the largest FCD- affected area emerged as the most reliable indicator of the EZ, combined either with calcifications or tubers. Significance: The epileptogenic zone in TSC patients harbors multiple dysplastic features, consistent with focal cortical dysplasia. A specific combination of these features can indicate the EZ and aid in pre- surgical MRI evaluation in epilepsy surgery candidates with TSC.

OBJECTIVE: Resective epilepsy surgery is an established treatment method for children with focal intractable epilepsy, but the use of this method introduces the risk of postsurgical motor deficits. Electrical stimulation mapping (ESM), used to define motor areas and pathways, frequently fails in children. The authors developed and tested a novel ESM protocol in children of all age categories. METHODS: The ESM protocol utilizes high-frequency electric cortical stimulation combined with continuous intraoperative motor-evoked potential (MEP) monitoring. The relationships between stimulation current intensity and selected presurgical and surgery-associated variables were analyzed in 66 children (aged 7 months to 18 years) undergoing 70 resective epilepsy surgeries in proximity to the motor cortex or corticospinal tracts. RESULTS: ESM elicited MEP responses in all children. Stimulation current intensity was associated with patient age at surgery and date of surgery (F value = 6.81, p < 0.001). Increase in stimulation current intensity predicted postsurgical motor deficits (F value = 44.5, p < 0.001) without effects on patient postsurgical seizure freedom (p > 0.05). CONCLUSIONS: The proposed ESM paradigm developed in our center represents a reliable method for preventing and predicting postsurgical motor deficits in all age groups of children. This novel ESM protocol may increase the safety and possibly also the completeness of epilepsy surgery. It could be adopted in pediatric epilepsy surgery centers.

Human perception and cognition are based predominantly on visual information processing. Much of the information regarding neuronal correlates of visual processing has been derived from functional imaging studies, which have identified a variety of brain areas contributing to visual analysis, recognition, and processing of objects and scenes. However, only two of these areas, namely the parahippocampal place area (PPA) and the lateral occipital complex (LOC), were verified and further characterized by intracranial electroencephalogram (iEEG). iEEG is a unique measurement technique that samples a local neuronal population with high temporal and anatomical resolution. In the present study, we aimed to expand on previous reports and examine brain activity for selectivity of scenes and objects in the broadband high-gamma frequency range (50–150 Hz). We collected iEEG data from 27 epileptic patients while they watched a series of images, containing objects and scenes, and we identified 375 bipolar channels responding to at least one of these two categories. Using K-means clustering, we delineated their brain localization. In addition to the two areas described previously, we detected significant responses in two other scene-selective areas, not yet reported by any electrophysiological studies; namely the occipital place area (OPA) and the retrosplenial complex. Moreover, using iEEG we revealed a much broader network underlying visual processing than that described to date, using specialized functional imaging experimental designs. ...

OBJECTIVE In this study, the authors aimed to determine 1) whether the use of intraoperative electrocorticography (ECoG) affects outcomes and complication rates of children undergoing resective epilepsy surgery; 2) which patient- and epilepsy-related variables might influence ECoG-based surgical strategy; and 3) what the predictors of epilepsy surgery outcomes are.

Background. We aimed first to describe trends in cognitive performance over time in a large patient cohort (n = 203) from a single tertiary centre for paediatric epilepsy surgery over the period of 16 years divided in two (developing-pre-2011 vs. established-post-2011). Secondly, we tried to identify subgroups of epilepsy surgery candidates with distinctive epilepsy-related characteristics that associate with their pre- and post-surgical cognitive performance. Thirdly, we analysed variables affecting pre-surgical and post-surgical IQ/DQ and their change (post- vs. pre-surgical).

Purpose: We assessed trends in spectrum of candidates, diagnostic algorithm, therapeutic approach and outcome of a pediatric epilepsy surgery program between 2000 and 2017.

Objective To evaluate the impact of generalized quasiperiodic epileptiform discharges ("hurdles") observed in non-rapid eye movement (NREM) sleep on cognitive function in children with intractable focal epilepsy. "Hurdles" pattern does not meet the criteria of the electrical status epilepticus in slow-wave sleep (ESES). Methods In a retrospective analysis, 24 patients with "hurdles" and their 24 peers matched for demographic and epilepsy-related variables were compared in terms of neuropsychological domains and electroencephalography (EEG)-derived quantifiers. Both "hurdles" and controls were children between 2 and 19 years of age who had intractable focal epilepsy evaluated as candidates of resective epilepsy surgery. Results Full-scale intelligence quotient/developmental quotient (FSIQ/DQ) (P = .002) and visuoconstructional skills (P = .004) were significantly lower in children with "hurdles" compared to controls. Patients with "hurdles" presented with higher interictal spike indexes in sleep (P < .001, median difference -0.9, 95% confidence interval [CI] -1.4, -0.6) and wakefulness (P < .001, median difference -0.3, 95% CI -0.5, -1). Relative time of sleep spindles in NREM sleep was significantly reduced (P < .001, median difference 0.1, 95% CI 0.0, 0.1) in the "hurdles" group. The time proportion of sleep spindles represented a significant positive (P = .008) and spike index of generalized spikes in sleep a significant negative explanatory variable (P = .004) of FSIQ/DQ scores. The proportion of seizure-free patients 2 years after epilepsy surgery did not differ significantly between the two groups (P = .19). Significance Although the "hurdles" pattern does not fulfill the criteria of ESES, it is associated with a pronounced cognitive dysfunction. Disturbed sleep structure marked by reduced sleep spindles and generalized spiking in sleep is associated with worse cognitive performance. Despite having a generalized nature, we did not find a lower probability of po

This paper aims to employ numerical simulations to assess the risk of cellular damage during the application of a novel paradigm of electrical stimulation mapping (ESM) used in neurosurgery. The core principle of the paradigm is the use of short, high-intensity and high-frequency stimulation pulses. We developed a complex numerical model and performed coupled electro-thermal transient simulations. The model was optimized by incorporating ESM electrodes’ resistance obtained during multiple intraoperative measurements and validated by comparing them with the results of temperature distribution measurement acquired by thermal imaging. The risk of heatinduced celluar damage was assessed by applying the Arrhenius equation integral on the computed time-dependent spatial distribution of temperature in the brain tissue. Our results suggest that the impact of the temperature increase during our novel ESM paradigm is non-destructive and thus prove that it is safe. The presented simulation results match the previously published thermographic measurement and histopathological examination of the stimulated brain tissue, and confirm the safety of novel ESM.

Aim We aimed to identify early predictors of intractable epilepsy, intellectual disability (ID) and autism spectrum disorders (ASD) in the cohort of TSC patients initially diagnosed with cardiac rhabdomyomas (CR). Method Over the period of twelve years we prospectively obtained clinical, neuropsychological, electrophysiological and neuroimaging data in a group of 22 TSC patients (9 females, 13 males) with the pre/perinatal diagnosis of CR, included to the study at the time of diagnosis. Afterwards, we statistically determined variables associated with ID, ASD and intractable epilepsy. Results Development of ID was predicted by severe epilepsy (a higher number of anti-epileptic drugs used), a higher number of dysplastic lesions on MRI, and abnormal background activity on EEG (p<0.05). Predictors of ASD included early developmental delay, abnormal background activity on EEG at the end of follow-up and a higher number of areas with dysplastic features on MRI (p<0.05). Intractable epilepsy was associated with a higher number of areas with dysplastic features on MRI, ID and with TSC2 genotype. Conclusion Adverse mental and clinical outcome was associated with intractable epilepsy and the severe anatomical brain involvement; therefore, our centre developed a tailored protocol for early identification of TSC patients at a higher risk of developing intractable epilepsy with its deleterious effect on cognitive outcome.

A standard procedure for continuous intraoperative monitoring of the integrity of the corticospinal tracts by eliciting muscle responses is the electric stimulation mapping (ESM). However, standard ESM protocols are ineffective in 20% of young children. We have developed a novel, highly efficient paradigm consisting of short-time burst (30 ms) of high frequency (500 Hz) and high peak current (≤100 mA), which may cause local tissue overheating. The presented safety control study was therefore designed. The infrared thermography camera captured to-be-resected cortex of 13 patients in vivo during ESM. Thermograms were image processed to reveal discrete ESM thermal effect of currents from 10 to 100 mA. Peak 100 mA currents induced a maximal increase in temperature of 3.1 °C, 1.23±0.72 °C in average. The warming correlated with stimulating electrode resistance ( p<0.001 ). The measurement uncertainty was estimated ± 1.01 ºC for the most skeptical conditions. The histopathological evaluation of stimulated tissue (performed in all cases) did not show any destructive changes. Our study demonstrates the ability of the thermographic camera to measure the discrete thermal effect of the ESM. The results provide evidence for the safety of the proposed protocol for full range currents with minimal risk of brain tissue damage.

Between seizures irritative network generates frequent brief synchronous activity, which manifests on the EEG as interictal epileptiform discharges (IEDs). Recent insights into the mechanism of IEDs at the microscopic level have demonstrated a high variance in the recruitment of neuronal populations generating IEDs and a high variability in the trajectories through which IEDs propagate across the brain. These phenomena represent one of the major constraints for precise characterization of network organization and for the utilization of IEDs during presurgical evaluations. We have developed a new approach to dissect human neocortical irritative networks and quantify their properties. We have demonstrated that irritative network has modular nature and it is composed of multiple independent sub-regions, each with specific IED propagation trajectories and differing in the extent of IED activity generated. The global activity of the irritative network is determined by long-term and circadian fluctuations in sub-region spatiotemporal properties. Also, the most active sub-region co-localizes with the seizure onset zone in 12/14 cases. This study demonstrates that principles of recruitment variability and propagation are conserved at the macroscopic level and that they determine irritative network properties in humans. Functional stratification of the irritative network increases the diagnostic yield of intracranial investigations with the potential to improve the outcomes of surgical treatment of neocortical epilepsy.

The cortical Electric Stimulation Mapping (ESM) procedure is used as a standard approach to localize and continuously monitor function of the eloquent cortex and corticospinal tract during neurosurgical intervention. However, eliciting motor responses using standard ESM paradigm is frequently difficult to young children. We have thus developed and tested a novel EMS protocol, which uses intense, high frequency and short stimulation pulses. However, the intense stimulation peak-peak current (up to 100 mA) possess the potential risk of tissue damage.The thermographic measurement was performed in four selected patients in vivo using the high-resolution thermographic camera during resective epilepsy surgery to verify the safety of the novel EMS paradigm. The EMS paradigm was systematically tested for pulse currents gradually increased from 10 to 100 mA. A moving thermographic picture was stabilized and emissivity was corrected for each pixel to reach the correct temperature interpretation. The results show a local temperature increase in the brain tissue close to the stimulation electrode during the ESM with current intensity above 40 mA. The 100 mA current caused the maximal temperature increase +0.4 °C. This value added to patient basal temperature is far under safety level 39 °C. Although the temperature increase observed around the stimulating electrode during our ESM paradigm is very low, we are aware that the borderline between electrode and cortex could not be reliably measured. Estimation of the electrical current density and the temperature distribution must be modeled using 3D numerical simulations and compared with the thermographic measurement in future work.

Intraoperative electrical stimulation mapping is used to localization and control of eloquent cortex and tracts condition during surgery intervention, however standard protocol is ineffective in pediatric patients. Therefore, the novel paradigm with higher amplitudes of stimulating currents must be applied. A 3D numerical model verified thermal effects of the stimulation to brain tissues. Numerical results of temperature distribution were compared to measurements.

High frequency oscillations (HFOs) are novel biomarker of epileptogenic tissue. HFOs are currently used to localize the seizure generating areas of the brain, delineate the resection and to monitor the disease activity. It is well established that spatiotemporal dynamics of HFOs can be modified by sleep-wake cycle. In this study we aimed to evaluate in detail circadian and ultradian changes in HFO dynamics using techniques of automatic HFO detection. For this purpose we have developed and implemented novel algorithm to automatic detection and analysis of HFOs in long-term intracranial recordings of six patients. In 5/6 patients HFO rates significantly increased during NREM sleep. The largest NREM related increase in HFO rates were observed in brain areas which spatially overlapped with seizure onset zone. Analysis of long-term recording revealed existence of ultradian changes in HFO dynamics. This study demonstrated reliability of automatic HFO detection in the analysis of long-term intracranial recordings in humans. Obtained results can foster practical implementation of automatic HFO detecting algorithms into presurgical examination, dramatically decrease human labour and increase the information yield of HFOs

High frequency oscillations (HFO) are believed to be a new specific biomarker of epileptogenic tissue. According to the reported findings, we hypothesized that HFOs occur more specific to the epileptogenic tissue. To test this hypothesis, we analyzed intracranial electroencephalograms (iEEG) of three subjects of animal epilepsy model. Overall 180 minutes of iEEG records were processed by the automatic high frequency activity detector and further analyzed. The dominant frequency of each segment was determined and categorized as ripples (80-200 Hz) or fast ripples (200-1000 Hz). Even though overall number of HFO detections predominate in the hemisphere where epileptic focus is located, in comparison of each brain structure separately our hypothesis cannot be fully confirmed.

In this study we try to find out if it is possible to differentiate type of focal cortical dysplasia by features obtained from intracranial EEG. We compare occurrence and rates of three biomarkers present in epilepsy in patients with focal cortical dysplasia type I and II. Case study is made on long term night records of 6 pediatric patients. Detection of interictal epileptiform discharges and high-frequency oscillations is made by automated algorithms, delta brush are marked visually. Position of lesion and electrodes inside were obtained from MRI. In individual rates were not found difference on significant level. No major significance were found, but as promising seem to be ratio inside to outside rates of high-frequency oscillations and presence of delta brush, which were found

Interictal epileptiform discharges (spikes, IEDs) are electrographic markers of epileptic tissue and their quantification is utilized in planning of surgical resection. Visual analysis of long-term multi-channel intracranial recordings is extremely laborious and prone to bias. Development of new and reliable techniques of automatic spike detection represents a crucial step towards increasing the information yield of intracranial recordings and to improve surgical outcome. In this study, we designed a novel and robust detection algorithm that adaptively models statistical distributions of signal envelopes and enables discrimination of signals containing IEDs from signals with background activity. This detector demonstrates performance superior both to human readers and to an established detector. It is even capable of identifying low-amplitude IEDs which are often missed by experts and which may represent an important source of clinical information. Application of the detector to non-epileptic intracranial data from patients with intractable facial pain revealed the existence of sharp transients with waveforms reminiscent of interictal discharges that can represent biological sources of false positive detections. Identification of these transients enabled us to develop and propose secondary processing steps, which may exclude these transients, improving the detector’s specificity and having important implications for future development of spike detectors in general.

The paper is focused on high frequency oscillations and their automated detection. This phenomena occurs in EEG of epileptic patients. Localization of these events lead to epileptogenic tissue. With more information neurologist can make better diagnose. Using detection algorithms can help obtain more information from signals. This should lead to better patient outcome. We discuss the possibility of using automatic detectors of high frequency activity in intracranial EEG as a support for human recording evaluation. Nowadays exist various algorithms for HFO detection with different approach and used methods. We have chosen and implemented several published detectors and developed our own algorithms. We compared their parameters on set of expert labeled records from patients with epilepsy. Results of algorithms are reviewed and discussed their quality output with respect to clinical practice and its utility in the diagnosis of epilepsy.

Vysokofrekvenční oscilace (HFO) jsou jedním z biomarkerů epileptogení tkáně. Vizuální hodnocení dlouhodobých EEG záznamů je velmi náročně nejen časově. Proto se nabízí možnost využití automatických algoritmů pro jejich detekci. V přechozích studiích jsme se zabývali jednotlivými detektory HFO a porovnáváním jejich kvality. V této studii navazujeme na naše zjištění a prezentujeme možný postup následného zpracování výsledků za účelem jejich zpřesnění. Námi navržená metoda třídí jednotlivé detekce podle jejich vzájemného výskytu v rámci elektrod i celého záznamu a také podle jejich četnosti. Předpokládáme, že se falešné detekce budou vyskytovat především ojediněle, zatím co hledané HFO se budou propagovat i do okolních tkání a budou detekovány v rámci jedné elektrody na více kanálech současně. Pomocí této metody jsme přehodnotili výsledky dvou pacientů z dětské neurologie a porovnali původní a nové výsledky s hodnocením neurologa.

Většina metod detekování vysokofrekvenčních událostí v elektroencefalogramu epileptických pacientů používá frekvenčně omezený signál, jehož změny sleduje. Detekční algoritmus popisovaný v této studii využívá odlišného přístupu a reaguje na změny amplitudově-frekvenčního spektra signálu. Proces zahrnuje výpočet normovaného spektra, zvýrazňujícího lokální odchylky, jehož stavy jsou dále klasifikovány. Detektor se zaměřuje především na oscilace v pásmu nad 200 Hz, označované jako Fast Ripple. Při testování bylo používáno především uměle modelovaných signálů EEG pomocí metody randomizace fáze, která je v textu také uvedena. Algoritmus je stále ve fázi vývoje a proto článek popisuje především principy a postupy vedoucí ke klasifikaci a uvádí pouze obecné nastavení. Optimalizace a statistické ověření vlastností algoritmu budou následující kroky dalšího vývoje.

Objective Localization of the epileptogenic zone (EZ) is challenging in children with tuberous sclerosis complex (TSC). We sought to ascertain whether brain MRI could identify the EZ in TSC patients independent of the clinical and diagnostic data. Methods Presurgical MRI's of 34 children with TSC who underwent epilepsy surgery at Miami Children's Hospital were retrospectively reevaluated by experts blinded to all other data. Changes typical of TSC (tubers, calcifications, cystic changes) and abnormalities of the perituberal cortex typical of focal cortical dysplasia (FCD) (increased cortical thickness, abnormal gyration, transmantle change, gray/white matter junction blurring) were identified and their localization was compared with the resection site. Sensitivity, specificity and accuracy of individual MRI features to localize the EZ were determined and statistically compared between postoperatively seizure-free and non-seizure-free patients as well as clusters of features typical of FCD and TSC. Results MRI alone correctly localized the resection cavity in all 19 postoperatively seizure-free patients and 12 of 15 non-seizure-free subjects. Sensitivity, specificity and accuracy of MRI features typical of FCD to localize EZ (90%, 96% and 96%, respectively) were superior to those typical of TCS (79%, 75% and 75%, p < 0.0001). Increased cortical thickness and abnormal gyral formation outside tubers occurred only in the resection site. Resection sites were better predicted by MRI in seizure-free than in non-seizure-free patients. Conclusion Thorough MRI evaluation identifies the EZ in a significant proportion of TSC patients. Epileptogenic regions were mostly characterized by “FCD-like” changes outside cortical tubers. The findings may have important practical consequences for surgical planning in TSC.

Vizuální označování vysokofrekvenčních oscilací (HFO) v intrakraniálních elektroencefalogramech (iEEG) je časově velmi náročné. Metoda, která tento proces urychluje, se skládá z výběru podezřelých úseků automatickými algoritmy a následné ohodnocení úseků lidským expertem. Celková časová náročnost je při tomto postupu přibližně čtvrtinová oproti standardnímu přístupu. Pro expertní hodnocení byla vyvinuta aplikace, která hodnotiteli poskytuje veškeré informace, potřebné pro kvalitní odborné hodnocení. Aplikace musí splňovat nároky na intuitivní ovládání, rychlost a stabilitu. Jelikož veškeré zpracování EEG signálů probíhá v prostředí MATLAB, tak i grafické rozhraní (GUI) pro tuto úlohu bylo navrženo v tomto prostředí.

Cílem chirurgické léčby epilepsie je odstranění mozkové tkáně představující tzv. epileptogenní zónu (EZ), která je primárně zodpovědná za vznik záchvatů. U řady pacientů je k přesnému určení EZ nezbytná dlouhodobá monitorace intrakraniálního EEG pomocí nitrolebních elektrod. Hranice EZ je následně stanovena průnikem výsledků neurozobrazovacích vyšetření a hodnocením intrakraniálního EEG. Výskyt epileptiformních grafoelementů v EEG v období mezi záchvaty je jedním z ukazatelů lokalizace epileptogenní tkáně. Kvantifikace epileptiformní aktivity (qEEG) v jednotlivých kanálech napomáhá lokalizaci a zpřesnění hranice EZ. Přesná prostorová interpretace výsledků automatického qEEG hodnocení vyžaduje koregistraci se zobrazovacími metodami, především s počítačovou tomografií (CT) a magnetickou rezonancí (MRI). V programovém prostředí MATLAB byl navržen postup, jež dovoluje fúzi medicínských obrazů s výsledky qEEG a vytváří tak multimodální zobrazení klinických a analytických dat.

Pochopení mechanizmů odpovědných za vznik epilepsie a epileptických záchvatů vyžaduje získání komplexních informací o časové a prostorové dynamice aktivity epileptických neuronálních populací. Elektrofyziologické techniky multikanálových záznamů představují jedny ze základních metod, které umožňují komplexní studium neuronální dynamiky v epileptickém mozku.

Hodnocení intrakraniálních EEG záznamů je velice komplexní a složitá záležitost. Při dlouhodobé monitoraci je nahráno velké množství dat a pro lékaře je téměř nemožné detailně prohlédnout a vyhodnotit celý záznam. Nalezení vysokofrekvenční aktivity, která má velmi malý odstup signál/šum je pak téměř nemožné. Proto se nabízí možnost vytvořit metody pro automatickou detekci této aktivity. Za tímto účelem již vzniklo několik algoritmů pro automatickou detekci tohoto fenoménu. V naší skupině jsme se také pokusili o pár vlastních návrhů. V tomto článku se pokusíme optimalizovat jednotlivé algoritmy a porovnat jejich zlepšení či citlivost vzhledem ke změnám jejich parametrů.

Interictal epileptiform discharges (spikes) represent electrographic marker of epileptogenic brain tissue. Besides ictal onsets, localization of interictal epileptiform discharges provides additional information to plan resective epilepsy surgery. The main goals of this study were: 1) to develop a reliable automatic algorithm to detect high and low amplitude interictal epileptiform discharges in intracranial EEG recordings and 2) to design a clustering method to extract spatial patterns of their propagation. For detection, we used a signal envelope modeling technique which adaptively identifies statistical parameters of signals containing spikes. Application of this technique to human intracranial EEG data demonstrated that it was superior to expert labeling and it was able to detect even small amplitude interictal epileptiform discharges. In the second task, detected spikes were clustered by principal component analysis according to their spatial distribution. Preliminary results showed that this unsupervised approach is able to identify distinct sources of interictal epileptiform discharges and has the potential to increase the yield of presurgical examination by improved delineation of the irritative zone.

High-frequency oscillations (HFOs) represent relatively new electrographic marker of epileptogenic tissue. It is starting to be used in presurgical examination to better plan surgical resection and to improve outcome of epilepsy surgery. Development of new techniques of unsupervised HFOs detection is required to further investigate the role of HFO in the pathophysiology of epilepsy and to increase the yield of presurgical examination. In this study we applied an envelope distribution modelling technique on experimental and human invasive data to detect HFOs. Application to experimental microelectrode recordings demonstrated satisfactory results with sensitivity 89.9% and false positive rate 2.1 per minute. Application of this algorithm to human invasive recordings achieved sensitivity 80%. High numbers of false positive detections required utilization of post-processing steps to eliminate the majority of them. This study shows that envelope distribution modelling represents a promising approach to detect HFOs in intracranial recordings. Advantages of this approach are quick adjustments to changes in background activity and resistance to signal non-stationarities. However, successful application to clinical practice requires development of secondary processing steps that will decrease the rate of false positive detections.

Tato část pojednává o možnostech využití RTG diagnostiky pro biologické vzorky

V minulosti byla prokázána souvislost mezi epiltogenní zónou a oblastí v níž se vyskytují vysokofrekvenční oscilace (HFO). Aby mohla být tato skutečnost plně využita je potřeba identifikovat a lokalizovat HFO v dlouhodobém elektroencefalogramu. Vizuální hodnocení je časově příliš náročné, tudíž je potřeba tuto úlohu řešit algoritmicky. Již bylo publikováno několik detektorů HFO, ale doposud neexistuje robustní obecně fungující metoda. V rámci naší výzkumné skupiny jsou vyvíjeny dva detektory s novým přístupem. V prezentované studii je popsána metodika statistického hodnocení HFO detektorů (senzitivita a pozitivní prediktivní hodnota), používané referenční množiny vytvořené metodami objektivizace hodnocení většího počtu hodnotitelů a výsledky statistických ukazatelů.

Abstract PURPOSE: Variable predictors of postsurgical seizure outcome have been reported in children with tuberous sclerosis complex (TSC). We analyzed a large surgical series of pediatric TSC patients in order to identify prognostic factors crucial for selection of subjects for epilepsy surgery. METHODS: Thirty-three children with TSC who underwent excisional epilepsy surgery at Miami Children's Hospital were retrospectively reviewed. A total of 29 clinical, neuropsychological, electroencephalography (EEG), magnetic resonance imaging (MRI), and surgical variables were analyzed and related to seizure outcomes. Univariate Barnard's exact test, Wilcoxon's rank-sum test, and multivariate statistical Cox's model were used to examine the significance of associations between the variables and seizure outcome. KEY FINDINGS: Eighteen patients (55%) have been seizure-free 2 years after (final) surgery; postoperative complications occurred in five subjects (15%). Complete removal of epileptogenic tissue detected by both MRI and intracranial EEG, regional scalp interictal EEG patterns, and agreement of interictal and ictal EEG localization were the most powerful predictors of seizure-free outcome. Other significant predictors included occurrence of regional scalp ictal EEG patterns, fewer brain regions affected by tubers, presence of preoperative hemiparesis, and one-stage surgery. Remaining factors such as age at seizure onset, incidence of infantile spasms or other seizure types, duration of epilepsy, seizure frequency, mental retardation, as well as types and extent of resections did not influence outcome. SIGNIFICANCE:Perioperative features rather than preoperative variables are the most important determinants of postsurgical seizure outcome in patients with TSC. Our findings may assist in the surgical management of these patients.

Vysokofrekvenční oscilace jsou jedním z příznaků epileptického EEG a lze je využí k přesnější diagnóze a lokalizaci epileptogenní tkáně. Pro zpracování epileptického EEG je v dnešní době již několik metod. Většina z nich je však pracuje na základě Fourierovy transformace. Ta předpokládá, že je signál stacionární a lineární, popřípadě pouze lineární pokud je využito krátkodobé fourierovy transformace. V tomto článku se zaměříme na využití ne tolik známé Hilbert-Huangovy transformace, která je navržena právě pro analýzu nestacionárních a nelineárních signálů. Navrhneme algoritmus pro detekci vysokofekvenční aktivity a ověříme zda je tato metoda pro tyto účely vhodná.

Tento článek pojednává o možnostech využití Hilbert-Huangovy transformace ve smyslu zpracování EEG dat pacientů s epilepsií. Tato metoda byla zvolena, protože přistupuje k signálu jako k nelineárnímu a nestacionárnímu procesu, jímž EEG záznam bezpochyby je. Článek je zaměřen především na empirickou modální dekompozici, která rozkládá signál na několik kopmonent s rozdílným frekvenčním obsahem. Diskutuje, zda se podle předpokladů v obsahu vyskytují hledané grafoelementy a zda je možné pomocí jednotlivých komponent určit jejich šíření do okolních kanálů.

This paper presents possibilities of correlation analysis for detection onset epileptic seizures in scalp electroencephalographic multichannel signals and describes benefits that system for automatic seizure detection could bring. In our implementation of system we use time segments of signals to calculate cross correlation over all channels and evaluate time progress of correlation coefficients. For evaluation of large amount of data from correlation maps is implemented method utilizing eigenvalues of matrix which calculates spectrum of the correlation matrix. For verification of function were used few EEG records from several patients. Results show that the method could be used as a support system for the onset seizure detection.

This article discuss problematic of using automated seizure detection as alarm or support in epilepsy screenings. Seizures are detected from scalp EEG which is less resistant to disturbance, but it is noninvasive. As method for detection use directed transfer function (DTF) a method based on multidimensional autoregressive models and discuss its reliability of seizure detection. Implementation in Matlab and discuss its results.

Vysokofrekvenční grafoelementy byly objeveny v EEG záznamech až poté, co měřící zařízení umožnily zaznamenat rychlou mozkovou aktivitu. Tyto události nazývané vysokofrekvenční oscilace (HFO) se v EEG záznamu objevují jak fyziologicky, tak patologicky. Větší význam jim neurologové přisuzují až v posledních dvou desetiletích. V tomto období byla také zjištěna spojitost mezi výskytem HFO a epilepsií. Za předpokladu, že tyto grafoelementy jsou vytvářeny právě epileptogenní tkání, by bylo možné takovou oblast na základě jejich výskytu lokalizovat. Za tímto účelem naše výzkumná skupina implementuje detektor těchto událostí. Tento detektor by měl být schopen HFO detekovat a umožnit tak objektivní hodnocení EEG záznamů.

Článek se zabývá možnostmi detekce a analýzy epileptiformních výbojů v peroperační ortikografii. Zařazení jednotlivých výbojů do klastrů umožňuje definovat oblasti, ve kterých výboje vznikají, a které jsou zodpovědné za vznik epileptických záchvatů. Lokalizace epileptiformních ložisek během operace může sloužit jako diagnostický nástroj pro určení rozsahu resekční oblasti při epileptochirurgické léčbě.

Článek se zabývá možnostmi využití rentgenu a CT diagnostiky voidů v pájených spojích.

Článek navazuje na předchozí kapitolu, zaměřuje se na problematiku nedestruktivního testování pomocí 3D CT technologie. Zmiňuje princip rekonstrukce informací o sledovaném objekt a problematiku artefaktů.

Tato kapitola navazuje na předchozí prezentací možností RTG inspekce na jiné než elektrotechnické vziorky

Statistické analýzy pozorování v klinické praxi jsou zásadní výzkumnou součástí vědecké práce. V rámci spolupráce technického a lékařského týmu v rámci vědecké skupiny ISARG vznikl i prostor pro podporu lékařské vědecké činnosti ze strany aplikace a hlavně implementace statistických metod pro měření validity pozorovaných skutečností a pro vzájemné srovnávání výsledků klinických studií mezi vědeckou komunitou. Nejinak tomu bylo i v případě klinické studie charakteristických rysů magnetické rezonance mozku epileptických pacientů s nálezem tuberozních skleróz za účelem lokalizace epileptogenních zón.

The aim of the project is to create suitable algorithms for detecting the presence, localization and extent of epileptogenic focus. The methods have been developed for providing an objective view of the diagnostic symptoms, and are based on search and evaluation of relations in the intracranial EEG signals recordings During the course of the project in the past year, we have focused mainly on the methods for detection of interictal spikes, methods using one-channel time-frequency analysis through Bayesian change-point detectors, methods using multi-channel detection based on multidimensional autoregressive models, and methods based on correlation analysis.

Use of computer methods of signal processing enables the use of advanced techniques for filtration of the recorded bio-signals. Usually, IIR or FIR digital filters are used for single modification; however, the filters have their limitations and do not always satisfy the processing requirements. Many more possibilities are offered by modification of the Fourier image of the signal, which allows almost unlimited changes of spectrum in the frequency domain. In compliance with several rules, it is possible to modify the signal as well as when using an ideal filter with almost infinite slope and infinite suppression in the stop-band. The filtration of this type is particularly suitable for the suppression of typical disturbing frequencies such as 50Hz mains.

Článek pojednáva o diagnostickém vybavení laboratoří LVR na FEL ČVUT. V článku je popsána vlastni technologie spolu s charakteristikou vlastniho diagnostického vybavení.

This paper presents a possibility of localization sources of epileptiform discharges in the Electrocorticography using time delay estimation. The work provides the first results of the proposed alorithm. It is presented on two patients with different time of propagation. The method can estimate delay under sample rate resolution.

This paper presents the possibility of early detection and localization of epileptogenic focus in the iEEG (intracranial Electroencephalography) signal using a method based on multidimensional autoregressive models. The work provides the first results of the method in the iEEG signal, and discusses technical aspects in terms of the suitability of the sampling frequency, AR model order and segmentation step.

Článek pojednáva o technologickém vybavení laboratoří LVR na FEL ČVUT. V článku je popsána vlastni technologie spolu s charakteristikou vlastniho technologického vybavení.

Various EEG recording systems are currently used in neurological departments. Due to the use of mostly commercial products for data acquisition and visualization by neurologists, the data storage formats or structures are not open. Some of the common software allows at least for export to text i.e. ASCII formats. The problem for further processing by tools such as Matlab is the very large data flow and memory demands for the ASCII data imports. The paper demonstrates the possibility of using a combination of a stream editor such as SED and the Matlab importer for extracting the useful information from the text export of one of the commercial EEG recordings software.

The aim of our research is to create algorithms suitable for detecting the presence, localization and determine the extent of epileptogenic focus. We developed experimentally methods for providing view of the diagnostic symptoms. The methods are based on search and evaluation of relations between the monitoring intracranial EEG signals. This paper shows our first approach to the problematic and also our preliminary results.

Automated signal processing of electroencephalographic (EEG) data is mostly very sophisticated and time consuming. Also the data amounts of the patients necessary to process are mostly high. Using a parallel signal processing techniques is suitable for saving the time. When the workgroup computing force consist only standard personal computers with standard equipment, a technique easy to implement is necessary. The one suitable technique is the Distributed Component Object Model (DCOM) mechanism that basically allows ActiveX clients to use remote ActiveX objects over a network. This paper describes one possibility of using a number of Matlab instances as ActiveX objects in school network working in synergy when the consequent is decreasing the computing time of EEG signal processing.

The paper describes an unusual approach to diagnose standard ignition subsystem of fuel engines. All the information about real state conditions of the ignition coils and sparks is taken from simple measurement of power consumption transient of the electric control systems of engine. The transient is processed by continuous wavelet transformation (CWT). Result of the CWT is used for a classifier. Classifier is supposed to work in learning and diagnostic modes. The main output of the classifier is two disjoint states meaning correct and incorrect functional work of the engine ignition system. The described method is going to be used as a part of standard information equipment of vehicles. The method could bring saving the costs for automobile manufacturer.

Today's diagnostic systems used in vehicles are based on separate diagnostic subsystems for each electronic or electric device. The basic idea of the project is to develop a centralized diagnostic method to cover most of functional systems. The proposed method would expedite the overall testing time and bring other significant advantages such as savings on test equipment. This paper deals with engine lighting equipment testing technique.

The objective of our work is to design and implement an automatic recognition system of mechanical wear out in a conductive non magnetic material. Especially in the flight industry, this is a very important issue as recognizing wear out in a material can avoid material failures. Nowadays this exploration needs highly skilled operators that using hand-operated eddy current testing (ECT) are able to recognize the flaws type and shape. With the introduction of a microprocessor based system the performance is enhanced by increasing the test speed while avoiding errors due to human failure like inexperience and inconsistency. In the present paper a computer based eddy current testing system is proposed. A description of the implemented prototype including some details on the digital processing techniques used and the measurements obtained for different artificial defects in an aluminum plate are presented.

The paper proposes a diagnostic method that deals with obtaining diagnostic information on parts of engine systems. Today's diagnostic systems used in vehicles are based on separate diagnostic subsystems for each electronic or electric device. The basic idea of the project is to develop a diagnostic method to cover all functional systems. The fundamental idea is that both electronic and electric devices in a vehicle have a relationship to communication control busses. Also each device can be described as an electrical consumer with its typical power consumption characteristics. Hence, the proposed diagnostic method is based on investigating transient profile on power lines of a tested device whilst changing its state from off to on. The short time fourier transform (STFT) processing is then applied to analyze the gained signal. The analysis results evaluate the condition of the engine lighting equipment.

The paper proposes a diagnostic method that deals with obtaining diagnostic information on parts of engine systems. Today's diagnostic systems used in vehicles are based on separate diagnostic subsystems for each electronic or electric device. The basic idea of the project is to develop a diagnostic method to cover all functional systems. The proposed method would expedite the overall testing time and bring other significant advantages such as savings on test equipment. The fundamental idea is that both electronic and electric devices in a vehicle have a relationship to communication control busses. Also each device can be described as an electrical consumer with its typical power consumption characteristics. Hence, the proposed diagnostic method is based on investigating transient profile on power lines of a tested device whilst changing its state from off to on. The image processing is then applied to analyze the gained signal.

In the flight industry, it is very important to have the possibility to check the mechanical wear-out of materials. The wear-out has to be precisely recognized to avoid failure. The object of our exploration is to design and implement an automatic recognition system of such mechanical wear-out. The eddy-current technique is used to detect cracks and faults and new ways in their recognition are going to be explored. Our sensor design is used.

Vehicle electronic systems have already reached a very high complexicity that is often invisible for general public. The high functional reliability of all these components and systems and also a rugged design of their diagnostic system is needed to achieve a transport safety. The diagnostic concept used in today's cars is based on separate diagnostic subsystems for each electronic or electric system (e.g. lighting bulbs, wiper motors and so on). The basic idea of the project "Centralised diagnostics of electronic and electric equipment in vehicles" is to minimize the number of diagnostic subsystems, decrease the diagnostics costs and icrease its flexibility. The main idea is based on the presumption that many faults of electric and electronic equipment in vehicles can be identified from their power consumption characteristics. Particularly it is supposed that the power supply transients can be used, occurring in time when the functional state of tested equipment is a