Orthogonal extended infomax algorithm.

Objective.The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster.Approach.Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the ICA unmixing matrix, leading to an orthogonal extended infomax algorithm (OgExtInf). The computational performance of OgExtInf was compared with original extended infomax and with two fast ICA algorithms: the popular FastICA and Picard, a preconditioned limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm belonging to the family of quasi-Newton methods.Main results.OgExtInf converges much faster than original extended infomax. For small-size electroencephalogram (EEG) data segments, as used for example in online EEG processing, OgExtInf is also faster than FastICA and Picard.Significance.OgExtInf may be useful for fast and reliable ICA, e.g. in online systems for epileptic spike and seizure detection or brain-computer interfaces.

Ongoing EEG artifact correction using blind source separation.

OBJECTIVE: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online. METHODS: The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts. RESULTS: The approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-the-art algorithms both in terms of artifact reduction rates and computation time. CONCLUSIONS: Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals. SIGNIFICANCE: The presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.

Fast evaluation of interictal spikes in long-term EEG by hyper-clustering.

PURPOSE: 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 density-based 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.

Sensitivity of a 29-Channel MEG Source Montage.

In this paper, we study the performance of a source montage corresponding to 29 brain regions reconstructed from whole-head magnetoencephalographic (MEG) recordings, with the aim of facilitating the review of MEG data containing epileptiform discharges. Test data were obtained by superposing simulated signals from 100-nAm dipolar sources to a resting state MEG recording from a healthy subject. Simulated sources were placed systematically to different cortical locations for defining the optimal regularization for the source montage reconstruction and for assessing the detectability of the source activity from the 29-channel MEG source montage. The signal-to-noise ratio (SNR), computed for each source from the sensor-level and source-montage signals, was used as the evaluation parameter. Without regularization, the SNR from the simulated sources was larger in the sensor-level signals than in the source montage reconstructions. Setting the regularization to 2% increased the source montage SNR to the same level as the sensor-level SNR, improving the detectability of the simulated events from the source montage reconstruction. Sources producing a SNR of at least 15 dB were visually detectable from the source-montage signals. Such sources are located closer than about 75 mm from the MEG sensors, in practice covering all areas in the grey matter. The 29-channel source montage creates more focal signals compared to the sensor space and can significantly shorten the detection time of epileptiform MEG discharges for focus localization.

EEG-fMRI: Ballistocardiogram Artifact Reduction by Surrogate Method for Improved Source Localization.

For the analysis of simultaneous EEG-fMRI recordings, it is vital to use effective artifact removal tools. This applies in particular to the ballistocardiogram (BCG) artifact which is difficult to remove without distorting signals of interest related to brain activity. Here, we documented the use of surrogate source models to separate the artifact-related signals from brain signals with minimal distortion of the brain activity of interest. The artifact topographies used for surrogate separation were created automatically using principal components analysis (PCA-S) or by manual selection of artifact components utilizing independent components analysis (ICA-S). Using real resting-state data from 55 subjects superimposed with simulated auditory evoked potentials (AEP), both approaches were compared with three established BCG artifact removal methods: Blind Source Separation (BSS), Optimal Basis Set (OBS), and a mixture of both (OBS-ICA). Each method was evaluated for its applicability for ERP and source analysis using the following criteria: the number of events surviving artifact threshold scans, signal-to-noise ratio (SNR), error of source localization, and signal variance explained by the dipolar model. Using these criteria, PCA-S and ICA-S fared best overall, with highly significant differences to the established methods, especially in source localization. The PCA-S approach was also applied to a single subject Berger experiment performed in the MRI scanner. Overall, the removal of BCG artifacts by the surrogate methods provides a substantial improvement for the analysis of simultaneous EEG-fMRI data compared to the established methods.

MEG versus EEG: influence of background activity on interictal spike detection.

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.

Prefrontal-cingulate activation during executive control: which comes first?

The Stroop test requires executive control functions, in particular inhibition of a learned routine (in this case, word reading). The spatiotemporal analysis of brain activation during Stroop task execution was performed in 16 healthy subjects using high-density event-related potentials (ERPs) and dipole source modeling (BESA software). Scalp ERP analysis revealed the neurophysiological substrate of the interference effect: first, a greater negativity in the incongruent as compared to the congruent and neutral conditions was found between 350 and 450 ms poststimulus over left frontocentral scalp regions. Source analysis of the difference wave (incongruent-congruent) indicated that a generator localized in left prefrontal cortex (PFC) contributed to this effect. Second, immediately after the first effect, a greater positivity in the incongruent as compared to the congruent and neutral conditions developed between 450 and 550 ms poststimulus over midline frontocentral scalp regions. A generator of this effect was located in right anterior cingulate cortex (ACC). ACC activation seems to follow the activation of PFC with some overlap between the two components. Possible interpretation of this finding is that PFC signals ACC when executive control is required and ACC implements the control.

Multimodal functional imaging of prolonged neurological deficits in a patient suffering from familial hemiplegic migraine.

The case of a patient with familial hemiplegic migraine (FHM) suffering from prolonged right sided hemiparesis and aphasia that persisted for more than 10 days is reported. The symptoms were accompanied by slowing of the magnetoencephalogram over the left hemisphere, which normalized parallel to the clinical improvement. Positron emission tomography obtained on the 6th day revealed glucose-hypometabolism (hemispheric difference > or =10%) in left hemisphere's fronto-basal cortex, caudate nucleus, and thalamus. In contrast, magnetic resonance imaging including perfusion and diffusion weighted imaging was normal and did not show significant alterations of cortical perfusion or water mobility during the episode. We hypothesize that this finding provides evidence for a primary neuronal dysfunction causing the prolonged neurological deficits in FHM.

Artifact correction of the ongoing EEG using spatial filters based on artifact and brain signal topographies.

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.

Advanced tools for digital EEG review: virtual source montages, whole-head mapping, correlation, and phase analysis.

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.

BESA source coherence: a new method to study cortical oscillatory coupling.

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.