Scalp EEG Recordings Research Articles

Accounting for microsaccadic artifacts in the EEG using independent component analysis and beamforming.

Neuronal activity in the gamma-band range was long considered a marker of object representation. However, scalp-recorded EEG activity in this range is contaminated by a miniature saccade-related muscle artifact. Independent component analysis (ICA) has been proposed as a method of removal of such artifacts. Alternatively, beamforming, a source analysis method in which potential sources of activity across the whole brain are scanned independently through the use of adaptive spatial filters, offers a promising method of accounting for the artifact without relying on its explicit removal. We present here the application of ICA-based correction to a previously published dataset. Then, using beamforming, we examine the effect of ICA correction on the scalp-recorded EEG signal and the extent to which genuine activity is recoverable before and after ICA correction. We find that beamforming attributes much of the scalp-recorded gamma-band signal before correction to deep frontal sources, likely the eye muscles, which generate the artifact related to each miniature saccade. Beamforming confirms that what is removed by ICA is predominantly this artifactual signal, and that what remains after correction plausibly originates in the visual cortex. Thus, beamforming allows researchers to confirm whether their removal procedures successfully removed the artifact. Our results demonstrate that ICA-based correction brings about general improvements in signal-to-noise ratio suggesting it should be used along with, rather than be replaced by, beamforming.

A Representational Similarity Analysis of the Dynamics of Object Processing Using Single-Trial EEG Classification.

The recognition of object categories is effortlessly accomplished in everyday life, yet its neural underpinnings remain not fully understood. In this electroencephalography (EEG) study, we used single-trial classification to perform a Representational Similarity Analysis (RSA) of categorical representation of objects in human visual cortex. Brain responses were recorded while participants viewed a set of 72 photographs of objects with a planned category structure. The Representational Dissimilarity Matrix (RDM) used for RSA was derived from confusions of a linear classifier operating on single EEG trials. In contrast to past studies, which used pairwise correlation or classification to derive the RDM, we used confusion matrices from multi-class classifications, which provided novel self-similarity measures that were used to derive the overall size of the representational space. We additionally performed classifications on subsets of the brain response in order to identify spatial and temporal EEG components that best discriminated object categories and exemplars. Results from category-level classifications revealed that brain responses to images of human faces formed the most distinct category, while responses to images from the two inanimate categories formed a single category cluster. Exemplar-level classifications produced a broadly similar category structure, as well as sub-clusters corresponding to natural language categories. Spatiotemporal components of the brain response that differentiated exemplars within a category were found to differ from those implicated in differentiating between categories. Our results show that a classification approach can be successfully applied to single-trial scalp-recorded EEG to recover fine-grained object category structure, as well as to identify interpretable spatiotemporal components underlying object processing. Finally, object category can be decoded from purely temporal information recorded at single electrodes.

Sparse cortical source localization using spatio-temporal atoms.

This paper addresses the problem of sparse localization of cortical sources from scalp EEG recordings. Localization algorithms use propagation model under spatial and/or temporal constraints, but their performance highly depends on the data signal-to-noise ratio (SNR). In this work we propose a dictionary based sparse localization method which uses a data driven spatio-temporal dictionary to reconstruct the measurements using Single Best Replacement (SBR) and Continuation Single Best Replacement (CSBR) algorithms. We tested and compared our methods with the well-known MUSIC and RAP-MUSIC algorithms on simulated realistic data. Tests were carried out for different noise levels. The results show that our method has a strong advantage over MUSIC-type methods in case of synchronized sources.

Automatic detection and removal of muscle artifacts from scalp EEG recordings in patients with epilepsy.

Automatic detection and removal of muscle artifacts plays an important role in long-term scalp electroencephalography (EEG) monitoring, and muscle artifact detection algorithms have been intensively investigated. This paper proposes an algorithm for automatic muscle artifacts detection and removal using canonical correlation analysis (CCA) and wavelet transform (WT) in epochs from long-term EEG recordings. The proposed method first performs CCA analysis and then conducts wavelet decomposition on the canonical components within a specific frequency range and selects a subset of the wavelet coefficients for subsequent processing. A set of features, including the mean of wavelet coefficients and the canonical component autocorrelation values, are extracted from the above analysis and subsequently used as input in a random forest (RF) classifier. The RF classifier produces a similarity measure between observations and selects a subset of the most important features by comparing the original data with a set of synthetic data that is constructed based on the latter. The RF predictor output is finally used in combination with unsupervised clustering algorithms to discriminate between contaminated and non-contaminated EEG epochs. The proposed method is evaluated in epochs of 30 min from scalp EEG recordings obtained from three patients with epilepsy and yields a sensitivity of 71% and 80%, as well as a specificity of 81% and 85% for k-means and spectral clustering, respectively.

A Wavelet-Based Artifact Reduction From Scalp EEG for Epileptic Seizure Detection.

This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e., 0.5-29 Hz) into account to separate artifacts from seizures. Different artifact templates have been simulated to mimic the most commonly appeared artifacts in real EEG recordings. The algorithm is applied on three sets of synthesized data including fully simulated, semi-simulated, and real data to evaluate both the artifact removal performance and seizure detection performance. The EEG features responsible for the detection of seizures from nonseizure epochs have been found to be easily distinguishable after artifacts are removed, and consequently, the false alarms in seizure detection are reduced. Results from an extensive experiment with these datasets prove the efficacy of the proposed algorithm, which makes it possible to use it for artifact removal in epilepsy diagnosis as well as other applications regarding neuroscience studies.

The neural speed of familiar face recognition

Rapidly recognizing familiar people from their faces appears critical for social interactions (e.g., to differentiate friend from foe). However, the actual speed at which the human brain can distinguish familiar from unknown faces still remains debated. In particular, it is not clear whether familiarity can be extracted from rapid face individualization or if it requires additional time consuming processing. We recorded scalp EEG activity in 28 subjects performing a go/no-go, famous/non-famous, unrepeated, face recognition task. Speed constraints were used to encourage subjects to use the earliest familiarity information available. Event related potential (ERP) analyses show that both the N170 and the N250 components were modulated by familiarity. The N170 modulation was related to behaviour: subjects presenting the strongest N170 modulation were also faster but less accurate than those who only showed weak N170 modulation. A complementary Multi-Variate Pattern Analysis (MVPA) confirmed ERP results and provided some more insights into the dynamics of face recognition as the N170 differential effect appeared to be related to a first transitory phase (transitory bump of decoding power) starting at around 140ms, which returned to baseline afterwards. This bump of activity was henceforth followed by an increase of decoding power starting around 200ms after stimulus onset. Overall, our results suggest that rather than a simple single-process, familiarity for faces may rely on a cascade of neural processes, including a coarse and fast stage starting at 140ms and a more refined but slower stage occurring after 200ms.

Neural Correlates of Three Promising Endophenotypes of Depression: Evidence from the EMBARC Study

Major depressive disorder (MDD) is clinically, and likely pathophysiologically, heterogeneous. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes. Guided by the NIMH Research Domain Criteria initiative, we used source localization of scalp-recorded EEG resting data to examine the neural correlates of three emerging endophenotypes of depression: neuroticism, blunted reward learning, and cognitive control deficits. Data were drawn from the ongoing multi-site EMBARC study. We estimated intracranial current density for standard EEG frequency bands in 82 unmedicated adults with MDD, using Low-Resolution Brain Electromagnetic Tomography. Region-of-interest and whole-brain analyses tested associations between resting state EEG current density and endophenotypes of interest. Neuroticism was associated with increased resting gamma (36.5-44 Hz) current density in the ventral (subgenual) anterior cingulate cortex (ACC) and orbitofrontal cortex (OFC). In contrast, reduced cognitive control correlated with decreased gamma activity in the left dorsolateral prefrontal cortex (dlPFC), decreased theta (6.5-8 Hz) and alpha2 (10.5-12 Hz) activity in the dorsal ACC, and increased alpha2 activity in the right dlPFC. Finally, blunted reward learning correlated with lower OFC and left dlPFC gamma activity. Computational modeling of trial-by-trial reinforcement learning further indicated that lower OFC gamma activity was linked to reduced reward sensitivity. Three putative endophenotypes of depression were found to have partially dissociable resting intracranial EEG correlates, reflecting different underlying neural dysfunctions. Overall, these findings highlight the need to parse the heterogeneity of MDD by focusing on promising endophenotypes linked to specific pathophysiological abnormalities.

Noninvasive Brain-Computer Interfaces Based on Sensorimotor Rhythms.

Brain-computer interfaces (BCIs) have been explored in the field of neuroengineering to investigate how the brain can use these systems to control external devices. We review the principles and approaches we have taken to develop a sensorimotor rhythm EEG based brain-computer interface (BCI). The methods include developing BCI systems incorporating the control of physical devices to increase user engagement, improving BCI systems by inversely mapping scalp-recorded EEG signals to the cortical source domain, integrating BCI with noninvasive neuromodulation strategies to improve learning, and incorporating mind-body awareness training to enhance BCI learning and performance. The challenges and merits of these strategies are discussed, together with recent findings. Our work indicates that the sensorimotor-rhythm-based noninvasive BCI has the potential to provide communication and control capabilities as an alternative to physiological motor pathways.

Noise robustness analysis of sparse representation based classification method for non-stationary EEG signal classification

Abstract In the electroencephalogram (EEG)-based brain–computer interface (BCI) systems, classification is an important signal processing step to control external devices using brain activity. However, scalp-recorded EEG signals have inherent non-stationary characteristics; thus, the classification performance is deteriorated by changing the background activity of the EEG during the BCI experiment. Recently, the sparse representation based classification (SRC) method has shown a robust classification performance in many pattern recognition fields including BCI. In this study, we aim to analyze noise robustness of the SRC method to evaluate the capability of the SRC for non-stationary EEG signal classification. For this purpose, we generate noisy test signals by adding a noise source such as random Gaussian and scalp-recorded background noise into the original motor imagery based EEG signals. Using the noisy test signals and real online-experimental dataset, we compare the classification performance of the SRC and support vector machine (SVM). Furthermore, we analyze the unique classification mechanism of the SRC. We observed that the SRC method provided better classification accuracy and noise robustness compared with the SVM method. In addition, the SRC has an inherent adaptive classification mechanism that makes it suitable for time-varying EEG signal classification for online BCI systems.

OP20 – 2281: Pre-Emptive EEG and synchronization analysis strategy for West syndrome

Objective To explore surrogate markers for early detection of West syndrome in patients with Down syndrome (DS) and to evaluate the correlation between the EEG phase synchrony (R index) and its spatial complexity, during developmental electric brain function in children with DS. Methods Retrospective analyses of sequential scalp EEG and clinical records of children with DS with early seizure disorders, with or without the EEG pattern of hypsarrhythmia, at the Hospital for Sick Children (Toronto, Ontario, Canada), from 1990 to 2012. Scalp EEG recordings were performed with the International 10–20 system of electrode placement. We extracted 10 second artifact-free epochs with 19 electrode EEG data (n=10) during stage II non-rapid eye movement (NREM) sleep for each patient. The R Index of the EEG synchronization was calculated using the Laplacian estimate followed by the Hilbert Transform. The spatial complexity of the R index was then calculated. Both measures were ascertained at 3, 6, 11 and 15 Hz. Results Children with DS, infantile spasms and hypsarrhythmia on their EEGs, showed higher R index of synchronization at all bandwidths (p Conclusion There was higher R index of EEG synchronization in patients with DS with hypsarrhythmia and also higher spatial complexity at 3Hz in DS without hypsarrhythmia. These data indicate that further synchronization and complexity analyses could be valuable in longitudinal EEG follow up to monitor earlier the evolution of hypsarrhythmia in children with DS. A prospective pre-emptive EEG strategy with R index of synchronization analyses is warranted, for earlier pharmacological treatments of patients with DS. The R index of EEG synchronization appears to be a useful surrogate marker before the final diagnosis of West syndrome.

Epileptic Seizure Detection Based on Partial Directed Coherence Analysis.

Long-term video EEG epilepsy monitoring can help doctors diagnose and cure epilepsy. The workload of doctors to read the EEG signals of epilepsy patients can be effectively reduced by automatic seizure detection. The application of partial directed coherence (PDC) analysis as mechanism for feature extraction in the scalp EEG recordings for seizure detection could reflect the physiological changes of brain activity before and after seizure onsets. In this study, a new approach on the basis of PDC was proposed to detect the seizure intervals of epilepsy patients. First of all, the multivariate autoregressive model was established for a moving window and the direction and intensity of information flow based on PDC analysis was calculated. Then, the outflow information related to certain EEG channel could be obtained by summing up the intensity of information flow propagated to other EEG channels in order to reduce the feature dimensionality. At last, according to the pathological features of epileptic seizures, the outflow information was regarded as the input vectors to a support vector machine classifier for discriminating interictal periods and ictal periods of EEG signals. The proposed method had achieved a good performance with the correct rate of 98.3%, the selectivity rate of 67.88%, the sensitivity rate of 91.44%, the specificity rate of 99.34%, and the average detection rate of 95.39%, which demonstrated that this method was suitable for detecting the seizure intervals of epilepsy patients. By comparing with other existing techniques, the proposed method based on PDC analysis achieved significant improvement in terms of seizure detection.

Olfaction modulates early neural responses to matching visual objects.

Sensory information is initially registered within anatomically and functionally segregated brain networks but is also integrated across modalities in higher cortical areas. Although considerable research has focused on uncovering the neural correlates of multisensory integration for the modalities of vision, audition, and touch, much less attention has been devoted to understanding interactions between vision and olfaction in humans. In this study, we asked how odors affect neural activity evoked by images of familiar visual objects associated with characteristic smells. We employed scalp-recorded EEG to measure visual ERPs evoked by briefly presented pictures of familiar objects, such as an orange, mint leaves, or a rose. During presentation of each visual stimulus, participants inhaled either a matching odor, a nonmatching odor, or plain air. The N1 component of the visual ERP was significantly enhanced for matching odors in women, but not in men. This is consistent with evidence that women are superior in detecting, discriminating, and identifying odors and that they have a higher gray matter concentration in olfactory areas of the OFC. We conclude that early visual processing is influenced by olfactory cues because of associations between odors and the objects that emit them, and that these associations are stronger in women than in men.

Rapid feedback processing in human nucleus accumbens and motor thalamus

The nucleus accumbens (NAcc) and thalamus are integral parts in models of feedback processing. Deep brain stimulation (DBS) has been successfully employed to alleviate symptoms of psychiatric conditions including obsessive-compulsive disorder (OCD) and Tourette's syndrome (TS). Common target structures are the NAcc and the ventral anterior and ventro-lateral nuclei (VA/VL) of the thalamus, for OCD and TS, respectively.The feedback related negativity (FRN) is an event-related potential associated with feedback processing reflecting posterior medial frontal cortex (pMFC) activity. Here we report on three cases where we recorded scalp EEG and local field potentials (LFP) from externalized electrodes located in the NAcc or thalamus (VA/VL) while patients engaged in a modified time estimation task, known to engage feedback processing and elicit the FRN. Additionally, scalp EEG were recorded from 29 healthy participants (HP) engaged in the same task.The signal in all structures (pMFC, NAcc, and thalamus) was differently modulated by positive and negative feedback. LFP activity in the NAcc showed a biphasic time course after positive feedback during the FRN time interval. Negative feedback elicited a much weaker and later response. In the thalamus a monophasic modulation was recorded during the FRN time interval. Again, this modulation was more pronounced after positive performance feedback compared to negative feedback. In channels outside the target area no modulation was observed. The surface-FRN was reliably elicited on a group level in HP and showed no significant difference following negative feedback between patients and HP.German Clinical Trial Register: Neurocognitive specification of dysfunctions within basal ganglia-cortex loops and their therapeutic modulation by deep brain stimulation in patients with obsessive compulsive disorder and Tourette syndrome, http://www.drks.de/DRKS00005316.

P300-based brain-computer interface (BCI) event-related potentials (ERPs): People with amyotrophic lateral sclerosis (ALS) vs. age-matched controls

ObjectiveBrain-computer interfaces (BCIs) aimed at restoring communication to people with severe neuromuscular disabilities often use event-related potentials (ERPs) in scalp-recorded EEG activity. Up to the present, most research and development in this area has been done in the laboratory with young healthy control subjects. In order to facilitate the development of BCI most useful to people with disabilities, the present study set out to: (1) determine whether people with amyotrophic lateral sclerosis (ALS) and healthy, age-matched volunteers (HVs) differ in the speed and accuracy of their ERP-based BCI use; (2) compare the ERP characteristics of these two groups; and (3) identify ERP-related factors that might enable improvement in BCI performance for people with disabilities. MethodsSixteen EEG channels were recorded while people with ALS or healthy age-matched volunteers (HVs) used a P300-based BCI. The subjects with ALS had little or no remaining useful motor control (mean ALS Functional Rating Scale-Revised 9.4 (±9.5SD) (range 0–25)). Each subject attended to a target item as the items in a 6×6 visual matrix flashed. The BCI used a stepwise linear discriminant function (SWLDA) to determine the item the user wished to select (i.e., the target item). Offline analyses assessed the latencies, amplitudes, and locations of ERPs to the target and non-target items for people with ALS and age-matched control subjects. ResultsBCI accuracy and communication rate did not differ significantly between ALS users and HVs. Although ERP morphology was similar for the two groups, their target ERPs differed significantly in the location and amplitude of the late positivity (P300), the amplitude of the early negativity (N200), and the latency of the late negativity (LN). ConclusionsThe differences in target ERP components between people with ALS and age-matched HVs are consistent with the growing recognition that ALS may affect cortical function. The development of BCIs for use by this population may begin with studies in HVs but also needs to include studies in people with ALS. Their differences in ERP components may affect the selection of electrode montages, and might also affect the selection of presentation parameters (e.g., matrix design, stimulation rate). SignificanceP300-based BCI performance in people severely disabled by ALS is similar to that of age-matched control subjects. At the same time, their ERP components differ to some degree from those of controls. Attention to these differences could contribute to the development of BCIs useful to those with ALS and possibly to others with severe neuromuscular disabilities.

The contribution of encoding and retrieval processes to proactive interference.

Proactive interference (PI) refers to the finding that memory for recently studied (target) material can be impaired by the prior study of other (nontarget) material. Previous accounts of PI differed in whether they attributed PI to impaired retrieval or impaired encoding. Here, we suggest an integrated encoding-retrieval account, which assigns a role for each of the 2 types of processes in buildup of PI. Employing a typical PI task, we examined (a) the role of encoding processes in PI by recording scalp EEG during study of nontarget and target lists, and (b) the role of retrieval processes in PI by measuring recall totals and response latencies in target list recall. In addition, we measured subjects' working memory capacity (WMC). Behaviorally, the PI effect arose in both recall totals and response latencies, indicating PI at the sampling and the recovery stage of recall. Neurally, we found an increase in electrophysiological activities in the theta frequency band (5-8 Hz) from nontarget to target list encoding, indicating an increase in memory load during target list encoding. The results demonstrate that impaired retrieval and impaired encoding can contribute to PI. They also show that WMC affects PI. For both encoding and retrieval processes, PI was reduced in high-WMC subjects, suggesting that these subjects are able to separate target from nontarget information and create stronger focus on the target material.

Electrophysiological Indices of Competition for Neural Resources in a Dual Working-Memory and Selective-Attention Task

Event Abstract Back to Event Electrophysiological Indices of Competition for Neural Resources in a Dual Working-Memory and Selective-Attention Task Dion Henare1, 2* and Paul Corballis1, 2 1 University of Auckland, School of Psychology, New Zealand 2 University of Auckland, Centre for Brain Research, New Zealand Visual selective attention performs an essential function in the human visual system, preferentially processing the most salient and relevant pieces of information in a scene. In this way, selective attention acts as a gate for high level, limited capacity processes like working memory. Evidence has also shown however that the contents of working memory can influence the performance of selective attention and this apparent interaction has led some to suggest that the two processes share the same limited neural resources. Previous studies have shown that increasing the load on an individual's working memory leads them to become more distractible in an attention task. In this experiment we recorded scalp EEG while participants perform a modified version of the localised attentional interference (LAI) paradigm under different levels of working memory load. The LAI paradigm allows us to show working-memory load effects on the amplitude of the attention-related lateralised components of the event-related brain potential (N2pc, Ptc, and SPCN), which have been related to target selection, distractor suppression, and working memory processes respectively. In addition, we have explored the EEG correlates of switching between the working-memory encoding task and the LAI task. We observe significant decreases in occipital alpha activity when participants initially switch to the LAI task following working-memory encoding, however this is only true for individuals with high baseline alpha power. This alpha decrease has been related to phasic arousal and alertness in previous task switching paradigms, and therefore its coincidence with behavioural differences in our task suggests that it may be an important factor to consider when studying the interaction of selective attention and working memory. Keywords: Attention, task-switching, working memory, Interaction, VEP, alpha modulation, lateralized ERP Conference: XII International Conference on Cognitive Neuroscience (ICON-XII), Brisbane, Queensland, Australia, 27 Jul - 31 Jul, 2014. Presentation Type: Poster Topic: Attention Citation: Henare D and Corballis P (2015). Electrophysiological Indices of Competition for Neural Resources in a Dual Working-Memory and Selective-Attention Task. Conference Abstract: XII International Conference on Cognitive Neuroscience (ICON-XII). doi: 10.3389/conf.fnhum.2015.217.00181 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 19 Feb 2015; Published Online: 24 Apr 2015. * Correspondence: Mr. Dion Henare, University of Auckland, School of Psychology, Auckland, New Zealand, Dion.Henare@auckland.ac.nz Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Dion Henare Paul Corballis Google Dion Henare Paul Corballis Google Scholar Dion Henare Paul Corballis PubMed Dion Henare Paul Corballis Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Scalp EEG brain functional connectivity networks in pediatric epilepsy

This study establishes a new data-driven approach to brain functional connectivity networks using scalp EEG recordings for classifying pediatric subjects with epilepsy from pediatric controls. Graph theory is explored on the functional connectivity networks of individuals where three different sets of topological features were defined and extracted for a thorough assessment of the two groups. The rater’s opinion on the diagnosis could also be taken into consideration when deploying the general linear model (GLM) for feature selection in order to optimize classification. Results demonstrate the existence of statistically significant (p<0.05) changes in the functional connectivity of patients with epilepsy compared to those of control subjects. Furthermore, clustering results demonstrate the ability to discriminate pediatric epilepsy patients from control subjects with an initial accuracy of 87.5%, prior to initiating the feature selection process and without taking into consideration the clinical rater’s opinion. Otherwise, leave-one-out cross validation (LOOCV) showed a significant increase in the classification accuracy to 96.87% in epilepsy diagnosis.

Comparison of different spatial transformations applied to EEG data: A case study of error processing

The purpose of this paper is to compare the effects of different spatial transformations applied to the same scalp-recorded EEG data. The spatial transformations applied are two referencing schemes (average and linked earlobes), the surface Laplacian, and beamforming (a distributed source localization procedure). EEG data were collected during a speeded reaction time task that provided a comparison of activity between error vs. correct responses. Analyses focused on time–frequency power, frequency band-specific inter-electrode connectivity, and within-subject cross-trial correlations between EEG activity and reaction time. Time–frequency power analyses showed similar patterns of midfrontal delta–theta power for errors compared to correct responses across all spatial transformations. Beamforming additionally revealed error-related anterior and lateral prefrontal beta-band activity. Within-subject brain–behavior correlations showed similar patterns of results across the spatial transformations, with the correlations being the weakest after beamforming. The most striking difference among the spatial transformations was seen in connectivity analyses: linked earlobe reference produced weak inter-site connectivity that was attributable to volume conduction (zero phase lag), while the average reference and Laplacian produced more interpretable connectivity results. Beamforming did not reveal any significant condition modulations of connectivity. Overall, these analyses show that some findings are robust to spatial transformations, while other findings, particularly those involving cross-trial analyses or connectivity, are more sensitive and may depend on the use of appropriate spatial transformations.

Oddball distractors demand attention: Neural and behavioral responses to predictability in the flanker task

Predictable and unpredictable distractors may differentially affect attention. We adapted the Eriksen flanker task by manipulating the probability with which specific flankers occurred. Subjects reported the orientation of briefly-presented targets while attempting to ignore four flanking items. Flankers had either standard (90% of trials) or oddball (10%) orientations. Congruent and incongruent configurations were equiprobable, as were target orientations. Oddball flanker orientations substantially enhanced congruency effects: performance was best when the target was congruent with oddball flankers and worst when it was incongruent. We recorded scalp EEG while subjects performed the task, and later computed ERPs timelocked to stimulus onset. Oddball flanker orientations evoked a visual mismatch negativity (vMMN). Subjects’ temperament predicted individual differences in vMMN magnitude. Orientation sensitivity predicted larger vMMNs; attentional selectivity predicted smaller. Behavioral and vMMN results indicate that subjects exploit distractor predictability to support more-effective active inhibition; oddballs disrupt this strategy. Despite subjects’ attempts to ignore the flankers, unexpected distractors strongly influence neural responses and behavioral performance.

Prospective multi-center study of an automatic online seizure detection system for epilepsy monitoring units

ObjectiveA method for automatic detection of epileptic seizures in long-term scalp-EEG recordings called EpiScan will be presented. EpiScan is used as alarm device to notify medical staff of epilepsy monitoring units (EMUs) in case of a seizure. MethodsA prospective multi-center study was performed in three EMUs including 205 patients. A comparison between EpiScan and the Persyst seizure detector on the prospective data will be presented. In addition, the detection results of EpiScan on retrospective EEG data of 310 patients and the public available CHB–MIT dataset will be shown. ResultsA detection sensitivity of 81% was reached for unequivocal electrographic seizures with false alarm rate of only 7perday. No statistical significant differences in the detection sensitivities could be found between the centers. The comparison to the Persyst seizure detector showed a lower false alarm rate of EpiScan but the difference was not of statistical significance. ConclusionsThe automatic seizure detection method EpiScan showed high sensitivity and low false alarm rate in a prospective multi-center study on a large number of patients. SignificanceThe application as seizure alarm device in EMUs becomes feasible and will raise the efficiency of video-EEG monitoring and the safety levels of patients.