Review and analysis of continuous EEG recordings may be impeded by physiological artifacts such as blinks, eye movements, or cardiac activity. Spatial filters based on artifact and brain signal topographies can remove artifacts completely without distortion of relevant brain activity. The authors describe the basic principle of artifact correction by spatial filtering and they review different approaches to estimate artifact and brain signal topographies. The main focus is on the preselection approach, which is fast enough to be applied while paging through the segments of a digital EEG recording. Examples of real EEG segments, containing epileptic seizure activity or interictal spikes contaminated by artifacts, show that spatial filtering by preselection can be a useful tool during EEG review. Advantages and disadvantages of the different spatial filter approaches are discussed.

This paper introduces source coherence, a new method for the analysis of cortical coherence using noninvasive EEG and MEG data. Brain electrical source analysis (BESA) is applied to create a discrete multiple source model. This model is used as a source montage to transform the recorded data from sensor level into brain source space. This provides source waveforms of the modeled brain regions as a direct measure for their activities on a single trial basis. The source waveforms are transformed into time-frequency space using complex demodulation. Magnitude-squared coherence between the brain sources reveals oscillatory coupling between sources. This procedure allows one to separate the time-frequency content of different brain regions even if their activities severely overlap at the surface. Thus, source coherence overcomes problems of localization and interpretation that are inherent to coherence analysis at sensor level. The principle of source coherence is illustrated using an EEG recording of an error-related negativity as an example. In this experiment the subject performed a visuo-motor task. Source coherence analysis revealed dynamical linking between posterior and central areas within the gamma-band around the time of button press at a post-stimulus latency of 200-300 ms.

No abstract

No abstract

No abstract

Digital EEG allows one to combine recorded EEG channels into new montages without the need to record new data. Using spherical splines, voltages can be estimated at any point on the head. This allows one to generate various montages with the recorded or virtual electrodes at standardized locations, to interpolate bad electrodes, and to generate topographic maps over the whole head. Simulations of EEG activity originating in various brain regions are used to illustrate the effects of known generators on various montages and on whole-head maps. Some properties of spatial filters are introduced, and it is shown how they can be used to develop source montages with signals that estimate the activity in specific brain regions. The usefulness and validity of a source montage designed to focus on temporal lobe activity is illustrated with simulations and examples of temporal lobe spikes and seizures. Additional tools such as cross-correlation among channels, fast Fourier transform, and phase maps are described. These tools are useful in estimating time lags between source channels and in interpreting propagating spike and seizure activity. In combination, these tools help to analyze and to enhance activities that may be hard to detect from the background scalp EEG in traditional montages.

The comparative sensitivity of EEG and magnetoencephalography (MEG) in the visual detection of focal epileptiform activity in simultaneous interictal sleep recordings were investigated. The authors examined 14 patients aged 3.5 to 17 years with localization-related epilepsy. Simultaneous 122-channel whole-head MEG and 33-channel EEG were recorded for 20 to 40 minutes during spontaneous sleep. The EEG and MEG data were separated and four blinded independent reviewers marked the presence and timing of epileptic discharges (ED) in the 28 data segments. EEG and MEG data were matched and spikes identified by at least three reviewers were classified in three categories according to the following criteria: type 1 MEG > EEG, type 2 EEG > MEG (type 1/2: difference of three or more raters), and type 3 EEG = MEG (three or more raters each). The presence of simultaneous sleep changes was visually determined for every single EEG-segment. Spikes with high spatiotemporal correlation were averaged and subjected to single dipole analysis of peak activity in EEG. Out of 4704 marked patterns, 1387 spikes fulfilled the above criteria. In fact, more spikes were unique to MEG (689) than to EEG (136) and to the combination of both modalities (562). ED were detected predominantly by MEG in eight patients and by EEG in two patients. The presence of vertex waves and spindles lead to a significantly higher number of spikes identified only in MEG. Averaging of type 1 spikes produced clear spike activity in EEG in 9 of 12 cases. On the contrary, only 2 of 10 type 2 spikes were visible in MEG after averaging. Dipoles of spikes visible in MEG showed a more tangential orientation compared with more radial dipoles of type 2 spikes. Spike characteristics, e.g., dipole orientation, are a key factor for a sole EEG representation. Exclusive MEG detection is more likely influenced by overlapping background activity in EEG. Because MEG is indifferent to radial activity, i.e., sleep changes, a higher ratio of spikes unique to MEG compared with EEG is detected in the case of overlapping sleep changes.

SUMMARYPurpose: The burden of reviewing long-term scalp electroencephalography (EEG) is not much alleviated by automated spike detection if thousands of events need to be inspected and mentally classified by the reviewer. This study investigated a novel technique of clustering and 24-h hyper-clustering on top of automated detection to assess whether fast review of focal interictal spike types was feasible and comparable to the spikes types observed during routine EEG review in epilepsy monitoring. Methods: Spike detection used a transformation of scalp EEG into 29 regional source activities and adaptive thresholds to increase sensitivity. Our rule-based algorithm estimated 18 parameters around each detected peak and combined multichannel detections into one event. Similarity measures were derived from equivalent location, scalp topography, and source waveform of each event to form clusters over 2-h epochs using a densitybased algorithm. Similar measures were applied to all 2-h clusters to form 24-h hyper-clusters. Independent raters evaluated electroencephalography data of 50 patients with epilepsy (25 children) using traditional visual spike review and optimized hyper-cluster inspection. Congruence between visual spike types and epileptiform hyper-clusters was assessed on a sublobar level using three-dimensional (3D) peak topographies. Key Findings: Visual rating found 126 different epileptiform spike types (2.5 per patient). Independently, 129 hyper-clusters were classified as epileptiform and originating in separate sublobar regions (2.6 per patient). Ninety-one percent of visual spike types matched with hyper-clusters (temporal lobe spikes 94%, extratemporal 89%). Conversely, 11% of hyper-clusters rated epileptiform had no corresponding visual spike type. Numbers were comparable in adults and children. On average, 15 hyper-clusters had to be inspected and rated per patient with an evaluation time of around 5 min. Significance: Hyper-clustering over 24 h provides an independent tool for rapid daily evaluation of interictal spikes in long-term video-EEG monitoring. If used in addition to routine review of 2-5 min EEG per hour, sensitivity and reliability in noninvasive diagnosis of focal epilepsy increases.