Search

OBJECTIVE: Patients with absence epilepsy sensitivity

Highlights • We demonstrate the feasibility of a fully automated REM sleep behaviour (RBD) screening tool using minimal sensors. • REM sleep is detected accurately and reliably in individuals with RBD, without EEG sensors. • Automated sleep staging was able to classify REM sleep with sufficient accuracy to allow for the accurate detection of RBD., Objective Rapid-Eye-Movement (REM) sleep behaviour disorder (RBD) is an early predictor of Parkinson’s disease, dementia with Lewy bodies, and multiple system atrophy. This study investigated the use of a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) followed by RBD detection without the need for cumbersome electroencephalogram (EEG) sensors. Methods Polysomnography signals from 50 participants with RBD and 50 age-matched healthy controls were used to evaluate this study. Three stage sleep classification was achieved using a random forest classifier and features derived from a combination of cost-effective and easy to use sensors, namely electrocardiogram (ECG), electrooculogram (EOG), and electromyogram (EMG) channels. Subsequently, RBD detection was achieved using established and new metrics derived from ECG and EMG channels. Results The EOG and EMG combination provided the optimal minimalist fully-automated performance, achieving 0.57 ± 0.19 kappa (3 stage) for sleep staging and an RBD detection accuracy of 0.90 ± 0.11, (sensitivity and specificity of 0.88 ± 0.13 and 0.92 ± 0.098, respectively). A single ECG sensor achieved three state sleep staging with 0.28 ± 0.06 kappa and RBD detection accuracy of 0.62 ± 0.10. Conclusions This study demonstrates the feasibility of using signals from a single EOG and EMG sensor to detect RBD using fully-automated techniques. Significance This study proposes a cost-effective, practical, and simple RBD identification support tool using only two sensors (EMG and EOG); ideal for screening purposes.

Premature babies are subjected to environmental stresses that can affect brain maturation and cause abnormal neurodevelopmental outcome later in life. Better understanding this link is crucial to developing a clinical tool for early outcome estimation. We defined maturational trajectories between the Electroencephalography (EEG)-derived ‘brain-age’ and postmenstrual age (the age since the last menstrual cycle of the mother) from longitudinal recordings during the baby’s stay in the Neonatal Intensive Care Unit. Data consisted of 224 recordings (65 patients) separated for normal and abnormal outcome at 9–24 months follow-up. Trajectory deviations were compared between outcome groups using the root mean squared error (RMSE) and maximum trajectory deviation (δmax). 113 features were extracted (per sleep state) to train a data-driven model that estimates brain-age, with the most prominent features identified as potential maturational and outcome-sensitive biomarkers. RMSE and δmax showed significant differences between outcome groups (cluster-based permutation test, p RMSE had a median (IQR) of 0.75 (0.60–1.35) weeks for normal outcome and 1.35 (1.15–1.55) for abnormal outcome, while δmax had a median of 0.90 (0.70–1.70) and 1.90 (1.20–2.90) weeks, respectively. Abnormal outcome trajectories were associated with clinically defined dysmature and disorganised EEG patterns, cementing the link between early maturational trajectories and neurodevelopmental outcome.

ispartof: J Neonatal Perinatal Med vol:15 issue:2 pages:411-426 ispartof: location:Netherlands status: published

Along the pathway from behavioral symptoms to the development of psychotic disorders sits the multivariate mediating brain. The functional organization and structural topography of large-scale neural mediators among patients with brain disorders, however, are not well understood. Here, we design a high-dimensional brain-wide functional mediation framework to investigate brain regions that intermediate between baseline behavioral symptoms and future conversion to full psychosis among individuals at clinical high risk (CHR). Using resting-state functional magnetic resonance imaging (fMRI) data from 263 CHR subjects, we extract an α brain atlas and a β brain atlas: the former underlines brain areas associated with prodromal symptoms and the latter highlights brain areas associated with disease onset. In parallel, we identify the P mediators and the N mediators that respectively facilitate or protect against developing brain disorders among subjects with more severe behavioral symptoms and quantify the effect of each neural mediator on disease development. Taken together, the α-β atlases and the P-N mediators paint a brain-wide picture of neural markers that are potentially regulating behavioral symptoms and the development of psychotic disorders and highlight a statistical framework that is useful to uncover large-scale intermediating variables in a regulatory biological organization.

Key Points Question Can a deep learning model accurately predict patient-specific estimated glomerular filtration rate (eGFR) ranges? Findings In this diagnostic study in a derivation cohort of 933 single kidney transplant recipients with 100 867 eGFR values and validation cohort of 1170 single kidney transplant recipients with 39 999 eGFR values, a sequence-to-sequence model was able to accurately predict patient-specific eGFR ranges within the first 3 months after transplant, based on the grafts’ previous eGFR values. Meaning Findings of this diagnostic study suggest that the patient-specific sequence predictions could be used in clinical practice to guide physicians to identify deviations from the expected intra-individual variability., Importance Like other clinical biomarkers, trajectories of estimated glomerular filtration rate (eGFR) after kidney transplant are characterized by intra-individual variability. These fluctuations hamper the distinction between alarming graft functional deterioration or harmless fluctuation within the patient-specific expected reference range of eGFR. Objective To determine whether a deep learning model could accurately predict the patient-specific expected reference range of eGFR after kidney transplant. Design, Setting, and Participants A multicenter diagnostic study consisted of a derivation cohort of 933 patients who received a kidney transplant between 2004 and 2013 with 100 867 eGFR measurements from University Hospitals Leuven, Belgium, and 2 independent test cohorts: with 39 999 eGFR measurements from 1 170 patients, 1 from University Hospitals Leuven, Belgium, receiving transplants between 2013 and 2018 and 1 from Hannover Medical School, Germany, receiving transplants between 2003 and 2007. Patients receiving a single kidney transplant, with consecutive eGFR measurements were included. Data were analyzed from February 2019 to April 2021. Exposures In the derivation cohort 100 867 eGFR measurements were available for analysis and 39 999 eGFR measurements from the independent test cohorts. Main Outcomes and Measures A sequence-to-sequence model was developed for prediction of a patient-specific expected range of eGFR, based on previous eGFR values. The primary outcome was the performance of the deep learning sequence-to-sequence model in the 2 independent cohorts. Results In this diagnostic study, a total of 933 patients in the training sets (mean [SD] age, 53.5 [13.3] years; 570 male [61.1%]) and 1170 patients in the independent test sets (cohort 1 [n = 621]: mean [SD] age, 58.5 [12.1] years; 400 male [64.4%]; cohort 2 [n = 549]: mean [SD] age, 50.1 [13.0] years; 316 male [57.6%]) who received a single kidney transplant most frequently from deceased donors, the sequence-to-sequence models accurately predicted future patient-specific eGFR trajectories within the first 3 months after transplant, based on the previous graft eGFR values (root mean square error, 6.4-8.9 mL/min/1.73 m2). The sequence-to-sequence model predictions outperformed the more conventional autoregressive integrated moving average prediction model, at all input/output number of eGFR values. Conclusions and Relevance In this diagnostic study, a sequence-to-sequence deep learning model was developed and validated for individual forecasting of kidney transplant function. The patient-specific sequence predictions could be used in clinical practice to guide physicians on deviations from the expected intra-individual variability, rather than relating the individual results to the reference range of the healthy population., This diagnostic study assesses whether a deep learning model could accurately predict the patient-specific expected reference range of estimated glomerular filtration rate after kidney transplant.

BackgroundProgressive supranuclear palsy (PSP), a neurodegenerative conditions may be difficult to discriminate clinically from idiopathic Parkinson’s disease (PD). It is critical that we are able to do this accurately and as early as possible in order that future disease modifying therapies for PSP may be deployed at a stage when they are likely to have maximal benefit. Analysis of gait and related tasks is one possible means of discrimination.Research QuestionHere we investigate a wearable sensor array coupled with machine learning approaches as a means of disease classification.Methods21 participants with PSP, 20 with PD, and 39 healthy control (HC) subjects performed a two minute walk, static sway test, and timed up-and-go task, while wearing an array of six inertial measurement units. The data were analysed to determine what features discriminated PSP from PD and PSP from HC. Two machine learning algorithms were applied, Logistic Regression (LR) and Random Forest (RF).Results17 features were identified in the combined dataset that contained independent information. The RF classifier outperformed the LR classifier, and allowed discrimination of PSP from PD with 86% sensitivity and 90% specificity, and PSP from HC with 90% sensitivity and 97% specificity. Using data from the single lumbar sensor only resulted in only a modest reduction in classification accuracy, which could be restored using 3 sensors (lumbar, right arm and foot). However for maximum specificity the full six sensor array was needed.SignificanceA wearable sensor array coupled with machine learning methods can accurately discriminate PSP from PD. Choice of array complexity depends on context; for diagnostic purposes a high specificity is needed suggesting the more complete array is advantageous, while for subsequent disease tracking a simpler system may suffice.

Background Avoidance to look others in the eye is a characteristic symptom of Autism Spectrum Disorders (ASD), and it has been hypothesised that quantitative monitoring of gaze patterns could be useful to objectively evaluate treatments. However, tools to measure gaze behaviour on a regular basis at a manageable cost are missing. In this paper, we investigated whether a smartphone-based tool could address this problem. Specifically, we assessed the accuracy with which the phone-based, state-of-the-art eye-tracking algorithm iTracker can distinguish between gaze towards the eyes and the mouth of a face displayed on the smartphone screen. This might allow mobile, longitudinal monitoring of gaze aversion behaviour in ASD patients in the future. Results We simulated a smartphone application in which subjects were shown an image on the screen and their gaze was analysed using iTracker. We evaluated the accuracy of our set-up across three tasks in a cohort of 17 healthy volunteers. In the first two tasks, subjects were shown different-sized images of a face and asked to alternate their gaze focus between the eyes and the mouth. In the last task, participants were asked to trace out a circle on the screen with their eyes. We confirm that iTracker can recapitulate the true gaze patterns, and capture relative position of gaze correctly, even on a different phone system to what it was trained on. Subject-specific bias can be corrected using an error model informed from the calibration data. We compare two calibration methods and observe that a linear model performs better than a previously proposed support vector regression-based method. Conclusions Under controlled conditions it is possible to reliably distinguish between gaze towards the eyes and the mouth with a smartphone-based set-up. However, future research will be required to improve the robustness of the system to roll angle of the phone and distance between the user and the screen to allow deployment in a home setting. We conclude that a smartphone-based gaze-monitoring tool provides promising opportunities for more quantitative monitoring of ASD. Electronic supplementary material The online version of this article (10.1186/s12938-019-0670-1) contains supplementary material, which is available to authorized users.

It is often stated that sleep deprivation is on the rise, with work suggested as a main cause. However, the evidence for increasing sleep deprivation comes from surveys using habitual sleep questions. An alternative source of information regarding sleep behaviour is time-use studies. This paper investigates changes in sleep time in the UK using the two British time-use studies that allow measuring "time in bed not asleep" separately from "actual sleep time". Based upon the studies presented here, people in the UK sleep today 43 min more than they did in the 1970s because they go to bed earlier (~30 min) and they wake up later (~15 min). The change in sleep duration is driven by night sleep and it is homogeneously distributed across the week. The former results apply to men and women alike, and to individuals of all ages and employment status, including employed individuals, the presumed major victims of the sleep deprivation epidemic and the 24/7 society. In fact, employed individuals have experienced a reduction in short sleeping of almost 4 percentage points, from 14.9% to 11.0%. There has also been a reduction of 15 percentage points in the amount of conflict between workers work time and their sleep time, as measured by the proportion of workers that do some work within their "ideal sleep window" (as defined by their own chronotype). ispartof: JOURNAL OF SLEEP RESEARCH vol:28 issue:1 ispartof: location:England status: published

OBJECTIVE: Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analysis of electroencephalography (EEG) to identify sleep stages is of great interest to clinicians. This automated sleep scoring can aid in optimizing neonatal care and assessing brain maturation. APPROACH: In this study, we designed and implemented an 18-layer convolutional neural network to discriminate quiet sleep from non-quiet sleep in preterm infants. The network is trained on 54 recordings from 13 preterm neonates and the performance is assessed on 43 recordings from 13 independent patients. All neonates had a normal neurodevelopmental outcome and the EEGs were recorded between 27 and 42 weeks postmenstrual age. MAIN RESULTS: The proposed network achieved an area under the mean and median ROC curve equal to 92% and 98%, respectively. SIGNIFICANCE: Our findings suggest that CNN is a suitable and fast approach to classify neonatal sleep stages in preterm infants. ispartof: Journal of Neural Engineering vol:15 issue:6 ispartof: location:England status: published

Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epoch, the novel framework jointly determines its label (classification) and its neighboring epochs’ labels (prediction) in the contextual output. While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways. First, it leverages the dependency among consecutive sleep epochs while surpassing the problems experienced with the common classification schemes. Second, even with a single model, the framework has the capacity to produce multiple decisions, which are essential in obtaining a good performance as in ensemble-of-models methods, with very little induced computational overhead. Probabilistic aggregation techniques are then proposed to leverage the availability of multiple decisions. To illustrate the efficacy of the proposed framework, we conducted experiments on two public datasets: Sleep-EDF Expanded (Sleep-EDF), which consists of 20 subjects, and Montreal Archive of Sleep Studies (MASS) dataset, which consists of 200 subjects. The proposed framework yields an overall classification accuracy of 82.3% and 83.6%, respectively. We also show that the proposed framework not only is superior to the baselines based on the common classification schemes but also outperforms existing deep-learning approaches. To our knowledge, this is the first work going beyond the standard single-output classification to consider multitask neural networks for automatic sleep staging. This framework provides avenues for further studies of different neural-network architectures for automatic sleep staging.

Newborn babies, particularly preterms, can exhibit early deviations in sleep maturation as seen by Electroencephalogram (EEG) recordings. This may be indicative of cognitive problems by school-age. The current ‘clinically-driven’ approach uses separate algorithms to first extract sleep states and then predict EEG ‘brain-age’. Maturational deviations are identified when the brain-age no longer matches the Postmenstrual Age (PMA, the age since the last menstrual cycle of the mother). However, the PMA range where existing sleep staging algorithms perform optimally, is limited, which subsequently limits the PMA range for brain-age prediction. We introduce a Bayesian Parametric Model (BPM) as a single end-to-end solution to directly estimate brain-age, modelling for sleep state maturation without requiring a separately optimized sleep staging algorithm. Comparison of this model with a traditional multi-stage approach, yields a similar Krippendorff’s $\alpha = 0.92$ (a performance measure ranging from 0 (chance agreement) to 1 (perfect agreement)) with the BPM performing better at younger ages

Background Objective behavioral markers of mental illness, often recorded through smartphones or wearable devices, have the potential to transform how mental health services are delivered and to help users monitor their own health. Linking objective markers to illness is commonly performed using population-level models, which assume that everyone is the same. The reality is that there are large levels of natural interindividual variability, both in terms of response to illness and in usual behavioral patterns, as well as intraindividual variability that these models do not consider. Objective The objective of this study was to demonstrate the utility of splitting the population into subsets of individuals that exhibit similar relationships between their objective markers and their mental states. Using these subsets, “group-personalized” models can be built for individuals based on other individuals to whom they are most similar. Methods We collected geolocation data from 59 participants who were part of the Automated Monitoring of Symptom Severity study at the University of Oxford. This was an observational data collection study. Participants were diagnosed with bipolar disorder (n=20); borderline personality disorder (n=17); or were healthy controls (n=22). Geolocation data were collected using a custom Android app installed on participants’ smartphones, and participants weekly reported their symptoms of depression using the 16-item quick inventory of depressive symptomatology questionnaire. Population-level models were built to estimate levels of depression using features derived from the geolocation data recorded from participants, and it was hypothesized that results could be improved by splitting individuals into subgroups with similar relationships between their behavioral features and depressive symptoms. We developed a new model using a Dirichlet process prior for splitting individuals into groups, with a Bayesian Lasso model in each group to link behavioral features with mental illness. The result is a model for each individual that incorporates information from other similar individuals to augment the limited training data available. Results The new group-personalized regression model showed a significant improvement over population-level models in predicting mental health severity (P

We develop a method for automated four-state sleep classification of preterm and term-born babies at term-age of 38-40 weeks postmenstrual age (the age since the last menstrual cycle of the mother) using multichannel electroencephalogram (EEG) recordings. At this critical age, EEG differentiates from broader quiet sleep (QS) and active sleep (AS) stages to four, more complex states, and the quality and timing of this differentiation is indicative of the level of brain development. However, existing methods for automated sleep classification remain focussed only on QS and AS sleep classification.EEG features were calculated from 16 EEG recordings, in 30 s epochs, and personalized feature scaling used to correct for some of the inter-recording variability, by standardizing each recording's feature data using its mean and standard deviation. Hidden Markov models (HMMs) and Gaussian mixture models (GMMs) were trained, with the HMM incorporating knowledge of the sleep state transition probabilities. Performance of the GMM and HMM (with and without scaling) were compared, and Cohen's kappa agreement calculated between the estimates and clinicians' visual labels.For four-state classification, the HMM proved superior to the GMM. With the inclusion of personalized feature scaling, mean kappa (±standard deviation) was 0.62 (±0.16) compared to the GMM value of 0.55 (±0.15). Without feature scaling, kappas for the HMM and GMM dropped to 0.56 (±0.18) and 0.51 (±0.15), respectively.This is the first study to present a successful method for the automated staging of four states in term-age sleep using multichannel EEG. Results suggested a benefit in incorporating transition information using an HMM, and correcting for inter-recording variability through personalized feature scaling. Determining the timing and quality of these states are indicative of developmental delays in both preterm and term-born babies that may lead to learning problems by school age.

Motor imagery (MI) combined with real-time electroencephalogram (EEG) feedback is a popular approach for steering brain–computer interfaces (BCI). MI BCI has been considered promising as add-on therapy to support motor recovery after stroke. Yet whether EEG neurofeedback indeed targets specific sensorimotor activation patterns cannot be unambiguously inferred from EEG alone. We combined MI EEG neurofeedback with concurrent and continuous functional magnetic resonance imaging (fMRI) to characterize the relationship between MI EEG neurofeedback and activation in cortical sensorimotor areas. EEG signals were corrected online from interfering MRI gradient and ballistocardiogram artifacts, enabling the delivery of real-time EEG feedback. Significantly enhanced task-specific brain activity during feedback compared to no feedback blocks was present in EEG and fMRI. Moreover, the contralateral MI related decrease in EEG sensorimotor rhythm amplitude correlated inversely with fMRI activation in the contralateral sensorimotor areas, whereas a lateralized fMRI pattern did not necessarily go along with a lateralized EEG pattern. Together, the findings indicate a complex relationship between MI EEG signals and sensorimotor cortical activity, whereby both are similarly modulated by EEG neurofeedback. This finding supports the potential of MI EEG neurofeedback for motor rehabilitation and helps to better understand individual differences in MI BCI performance.

Objective The study compared two channel-reduction approaches in order to investigate the effects of systematic motor imagery (MI) neurofeedback practice in an everyday environment using a very user-friendly EEG system consisting of individualized caps and highly portable hardware. Methods Sixteen BCI novices were trained over four consecutive days to imagine left and right hand movements while receiving feedback. The most informative bipolar channels for use on the subsequent days were identified on the first day for each individual based on a high-density online MI recording. Results Online classification accuracy on the first day was 85.1% on average (range: 64.7–97.7%). Offline an individually-selected bipolar channel pair based on common spatial patterns significantly outperformed a pair informed by independent component analysis and a standard 10–20 pair. From day 2 to day 4 online MI accuracy increased significantly (day 2: 69.1%; day 4: 73.3%), which was mostly caused by a reduction in ipsilateral event-related desynchronization of sensorimotor rhythms. Conclusion The present study demonstrates that systematic MI practice in an everyday environment with a user-friendly EEG system results in MI learning effects. Significance These findings help to bridge the gap between elaborate laboratory studies with healthy participants and efficient home or hospital based MI neurofeedback protocols.

Objective Cochlear implants (CIs) can partially restore hearing, but the cortical changes underlying auditory rehabilitation are not well understood. Methods This prospective longitudinal study used electroencephalography (EEG) to examine the temporal dynamics of changes in the auditory cortex contralateral and ipsilateral to the CI. Postlingually deafened CI recipients ( N = 11; mean: 59 years) performed an auditory frequency discrimination task after Results The CI users revealed a remarkable improvement in auditory discrimination ability which was most pronounced over the first eight weeks of CI experience. At the same time, CI users developed N1 auditory event-related potentials (AEP) with significantly enhanced amplitude and decreased latency, both in the auditory cortex contralateral and ipsilateral to the CI. A relationship was found between the duration of deafness and the ipsilateral AEP latency. Conclusions Postlingually deafened adult CI users show rapid adaptation of the bilateral auditory cortex. Cortical plasticity is limited after long duration of auditory deprivation. Significance The finding of rapid and limited cortical changes in adult CI recipients may be of clinical relevance and can help estimate the role of plasticity for therapeutic gain.

To study the dynamics of contour integration in the human brain, we simultaneously acquired EEG and fMRI data while participants were engaged in a passive viewing task. The stimuli were Gabor arrays with some Gabor elements positioned on the contour of an embedded shape, in three conditions: with local and global structure (perfect contour alignment), with global structure only (orthogonal orientations interrupting the alignment), or without contour. By applying JointICA to the EEG and fMRI responses of the subjects, new insights could be obtained that cannot be derived from unimodal recordings. In particular, only in the global structure condition, an ERP peak around 300ms was identified that involved a loop from LOC to the early visual areas. This component can be interpreted as being related to the verification of the consistency of the different local elements with the globally defined shape, which is necessary when perfect local-to-global alignment is absent. By modifying JointICA, a quantitative comparison of brain regions and the time-course of their interplay were obtained between different conditions. More generally, we provide additional support for the presence of feedback loops from higher areas to lower level sensory regions.

Objective Circadian and sleep dysfunction have long been symptomatic hallmarks of a variety of devastating neurodegenerative conditions. The gold standard for sleep monitoring is overnight sleep in a polysomnography (PSG) laboratory. However, this method has several limitations such as availability, cost and being labour-intensive. In recent years there has been a heightened interest in home-based sleep monitoring via wearable sensors. Our objective was to demonstrate the use of printed electrode technology as a novel platform for sleep monitoring. Approach Printed electrode arrays offer exciting opportunities in the realm of wearable electrophysiology. In particular, soft electrodes can conform neatly to the wearer's skin, allowing user convenience and stable recordings. As such, soft skin-adhesive non-gel-based electrodes offer a unique opportunity to combine electroencephalography (EEG), electromyography (EMG), electrooculography (EOG) and facial EMG capabilities to capture neural and motor functions in comfortable non-laboratory settings. In this investigation temporary-tattoo dry electrode system for sleep staging analysis was designed, implemented and tested. Main results EMG, EOG and EEG were successfully recorded using a wireless system. Stable recordings were achieved both at a hospital environment and a home setting. Sleep monitoring during a 6 h session shows clear differentiation of sleep stages. Significance The new system has great potential in monitoring sleep disorders in the home environment. Specifically, it may allow the identification of disorders associated with neurological disorders such as rapid eye movement (REM) sleep behavior disorder.

The decomposition of high-density surface EMG (HD-sEMG) interference patterns into the contribution of motor units is still a challenging task. We introduce a new, fast solution to this problem. The method uses a data-driven approach for selecting a set of electrodes to enable discrimination of present motor unit action potentials (MUAPs). Then, using shapes detected on these channels, the hierarchical clustering algorithm as reported by Quian Quiroga et al. (Neural Comput 16:1661-1687, 2004) is extended for multichannel data in order to obtain the motor unit action potential (MUAP) signatures. After this first step, more motor unit firings are obtained using the extracted signatures by a novel demixing technique. In this demixing stage, we propose a time-efficient solution for the general convolutive system that models the motor unit firings on the HD-sEMG grid. We constrain this system by using the extracted signatures as prior knowledge and reconstruct the firing patterns in a computationally efficient way. The algorithm performance is successfully verified on simulated data containing up to 20 different MUAP signatures. Moreover, we tested the method on real low contraction recordings from the lateral vastus leg muscle by comparing the algorithm's output to the results obtained by manual analysis of the data from two independent trained operators. The proposed method showed to perform about equally successful as the operators.

OBJECTIVE: In the past few years there has been a growing interest in studying brain functioning in natural, real-life situations. Mobile EEG allows to study the brain in real unconstrained environments but it faces the intrinsic challenge that it is impossible to disentangle observed changes in brain activity due to increase in cognitive demands by the complex natural environment or due to the physical involvement. In this work we aim to disentangle the influence of cognitive demands and distractions that arise from such outdoor unconstrained recordings. APPROACH: We evaluate the ERP and single trial characteristics of a three-class auditory oddball paradigm recorded in outdoor scenario's while peddling on a fixed bike or biking freely around. In addition we also carefully evaluate the trial specific motion artifacts through independent gyro measurements and control for muscle artifacts. MAIN RESULTS: A decrease in P300 amplitude was observed in the free biking condition as compared to the fixed bike conditions. Above chance P300 single-trial classification in highly dynamic real life environments while biking outdoors was achieved. Certain significant artifact patterns were identified in the free biking condition, but neither these nor the increase in movement (as derived from continuous gyrometer measurements) can explain the differences in classification accuracy and P300 waveform differences with full clarity. The increased cognitive load in real-life scenarios is shown to play a major role in the observed differences. SIGNIFICANCE: Our findings suggest that auditory oddball results measured in natural real-life scenarios are influenced mainly by increased cognitive load due to being in an unconstrained environment. journal_title: Journal of Neural Engineering article_type: paper article_title: Mobile EEG on the bike: disentangling attentional and physical contributions to auditory attention tasks copyright_information: © 2016 IOP Publishing Ltd date_received: 2016-01-26 date_accepted: 2016-06-07 date_epub: 2016-06-28 ispartof: Journal of Neural Engineering vol:13 issue:4 ispartof: location:England status: published

Childhood pneumonia is the leading cause of death of children under the age of 5 years globally. Diagnostic information on the presence of infection, severity and aetiology (bacterial versus viral) is crucial for appropriate treatment. However, the derivation of such information requires advanced equipment (such as X-rays) and clinical expertise to correctly assess observational clinical signs (such as chest indrawing); both of these are often unavailable in resource-constrained settings. In this study, these challenges were addressed through the development of a suite of data mining tools, facilitating automated diagnosis through quantifiable features. Findings were validated on a large dataset comprising 780 children diagnosed with pneumonia and 801 age-matched healthy controls. Pneumonia was identified via four quantifiable vital signs (98.2% sensitivity and 97.6% specificity). Moreover, it was shown that severity can be determined through a combination of three vital signs and two lung sounds (72.4% sensitivity and 82.2% specificity); addition of a conventional biomarker (C-reactive protein) further improved severity predictions (89.1% sensitivity and 81.3% specificity). Finally, we demonstrated that aetiology can be determined using three vital signs and a newly proposed biomarker (lipocalin-2) (81.8% sensitivity and 90.6% specificity). These results suggest that a suite of carefully designed machine learning tools can be used to support multi-faceted diagnosis of childhood pneumonia in resource-constrained settings, compensating for the shortage of expensive equipment and highly trained clinicians.

OBJECTIVE: One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. APPROACH: We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. MAIN RESULTS: The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. SIGNIFICANCE: The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches. journal_title: Journal of Neural Engineering article_type: paper article_title: Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase copyright_information: © 2016 IOP Publishing Ltd date_received: 2015-07-17 date_accepted: 2015-12-23 date_epub: 2016-02-01 ispartof: Journal of Neural Engineering vol:13 issue:2 pages:026005-026005 ispartof: location:England status: published

Insights from both lesion and neuroimaging studies increasingly substantiate the view that the human cerebellum not only serves motor control but also supports various cognitive processes. Higher cognitive functions like working memory or executive control have been associated with the phylogenetically younger parts of the cerebellum, crus I and crus II. Functional connectivity studies corroborate this notion as activation of the cerebellum correlates with activity in numerous areas of the cerebral cortex. Moreover, these cerebrocerebellar loops were shown to be topographically organized. We used an attention-to-motion paradigm to elaborate on the effective connectivity of cerebellar crus I during visual attention. Psychophysiological interaction analyses demonstrated enhanced connectivity of the cerebellum—during attention—with dorsal visual stream regions including posterior parietal cortex (PPC) and left secondary visual cortex (V5). Dynamic causal modeling revealed a modulation of the connections from V5 to PPC and from crus I to V5 by attention. Remarkably, the influence which V5 exerted on PPC was reduced during attention, resulting in a suppression of the sensitivity of PPC to bottom-up information. Moreover, the sensitivity of V5 populations to inputs from crus I was increased under attention. This might underscore the presumed role of the cerebellum as a state estimator that provides hierarchically lower regions (V5) with top-down predictions, which in turn might be based on endogenous inputs from PPC to the cerebellum. These results are in line with formulations of attention in predictive coding, where attention increases the precision or sensitivity of hierarchically lower neuronal populations that may encode prediction error.

Electrical artifacts caused by the cochlear implant (CI) contaminate electroencephalographic (EEG) recordings from implanted individuals and corrupt auditory evoked potentials (AEPs). Independent component analysis (ICA) is efficient in attenuating the electrical CI artifact and AEPs can be successfully reconstructed. However the manual selection of CI artifact related independent components (ICs) obtained with ICA is unsatisfactory, since it contains expert-choices and is time consuming. We developed a new procedure to evaluate temporal and topographical properties of ICs and semi-automatically select those components representing electrical CI artifact. The CI Artifact Correction (CIAC) algorithm was tested on EEG data from two different studies. The first consists of published datasets from 18 CI users listening to environmental sounds. Compared to the manual IC selection performed by an expert the sensitivity of CIAC was 91.7% and the specificity 92.3%. After CIAC-based attenuation of CI artifacts, a high correlation between age and N1–P2 peak-to-peak amplitude was observed in the AEPs, replicating previously reported findings and further confirming the algorithm's validity. In the second study AEPs in response to pure tone and white noise stimuli from 12 CI users that had also participated in the other study were evaluated. CI artifacts were attenuated based on the IC selection performed semi-automatically by CIAC and manually by one expert. Again, a correlation between N1 amplitude and age was found. Moreover, a high test–retest reliability for AEP N1 amplitudes and latencies suggested that CIAC-based attenuation reliably preserves plausible individual response characteristics. We conclude that CIAC enables the objective and efficient attenuation of the CI artifact in EEG recordings, as it provided a reasonable reconstruction of individual AEPs. The systematic pattern of individual differences in N1 amplitudes and latencies observed with different stimuli at different sessions, strongly suggests that CIAC can overcome the electrical artifact problem. Thus CIAC facilitates the use of cortical AEPs as an objective measurement of auditory rehabilitation.

Since several years, neuroscience research started to focus on multimodal approaches. One such multimodal approach is the combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). However, no standard integration procedure has been established so far. One promising data-driven approach consists of a joint decomposition of event-related potentials (ERPs) and fMRI maps derived from the response to a particular stimulus. Such an algorithm (joint independent component analysis or JointICA) has recently been proposed by Calhoun et al. (2006). This method provides sources with both a fine spatial and temporal resolution, and has shown to provide meaningful results. However, the algorithm's performance has not been fully characterized yet, and no procedure has been proposed to assess the quality of the decomposition. In this paper, we therefore try to answer why and how JointICA works. We show the performance of the algorithm on data obtained in a visual detection task, and compare the performance for EEG recorded simultaneously with fMRI data and for EEG recorded in a separate session (outside the scanner room). We perform several analyses in order to set the necessary conditions that lead to a sound decomposition, and to give additional insights for exploration in future studies. In that respect, we show how the algorithm behaves when different EEG electrodes are used and we test the robustness with respect to the number of subjects in the study. The performance of the algorithm in all the experiments is validated based on results from previous studies. ispartof: NeuroImage vol:60 issue:2 pages:1171-1185 ispartof: location:United States status: published

In the multisensory environment, inputs to each sensory modality are rarely independent. Sounds often follow a visible action or event. Here we present behaviorally relevant evidence from the human EEG that visual input prepares the auditory system for subsequent auditory processing by resetting the phase of neuronal oscillatory activity in auditory cortex. Subjects performed a simple auditory frequency discrimination task using paired but asynchronous auditory and visual stimuli. Auditory cortex activity was modeled from the scalp-recorded EEG using spatiotemporal dipole source analysis. Phase resetting activity was assessed using time–frequency analysis of the source waveforms. Significant cross-modal phase resetting was observed in auditory cortex at low alpha frequencies (8–10 Hz) peaking 80 ms after auditory onset, at high alpha frequencies (10–12 Hz) peaking at 88 ms, and at high theta frequencies (∼7 Hz) peaking at 156 ms. Importantly, significant effects were only evident when visual input preceded auditory by between 30 and 75 ms. Behaviorally, cross-modal phase resetting accounted for 18% of the variability in response speed in the auditory task, with stronger resetting overall leading to significantly faster responses. A direct link was thus shown between visual-induced modulations of auditory cortex activity and performance in an auditory task. The results are consistent with a model in which the efficiency of auditory processing is improved when natural associations between visual and auditory inputs allow one input to reliably predict the next.

Multimodal approaches are of growing interest in the study of neural processes. To this end much attention has been paid to the integration of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data because of their complementary properties. However, the simultaneous acquisition of both types of data causes serious artifacts in the EEG, with amplitudes that may be much larger than those of EEG signals themselves. The most challenging of these artifacts is the ballistocardiogram (BCG) artifact, caused by pulse-related electrode movements inside the magnetic field. Despite numerous efforts to find a suitable approach to remove this artifact, still a considerable discrepancy exists between current EEG-fMRI studies. This paper attempts to clarify several methodological issues regarding the different approaches with an extensive validation based on event-related potentials (ERPs). More specifically, Optimal Basis Set (OBS) and Independent Component Analysis (ICA) based methods were investigated. Their validation was not only performed with measures known from previous studies on the average ERPs, but most attention was focused on task-related measures, including their use on trial-to-trial information. These more detailed validation criteria enabled us to find a clearer distinction between the most widely used cleaning methods. Both OBS and ICA proved to be able to yield equally good results. However, ICA methods needed more parameter tuning, thereby making OBS more robust and easy to use. Moreover, applying OBS prior to ICA can optimize the data quality even more, but caution is recommended since the effect of the additional ICA step may be strongly subject-dependent. ispartof: NeuroImage vol:50 issue:3 pages:920-934 ispartof: location:United States status: published

Objective. Recent studies have provided evidence that temporal envelope driven speech decoding from high-density electroencephalography (EEG) and magnetoencephalography recordings can identify the attended speech stream in a multi-speaker scenario. The present work replicated the previous high density EEG study and investigated the necessary technical requirements for practical attended speech decoding with EEG. Approach. Twelve normal hearing participants attended to one out of two simultaneously presented audiobook stories, while high density EEG was recorded. An offline iterative procedure eliminating those channels contributing the least to decoding provided insight into the necessary channel number and optimal cross-subject channel configuration. Aiming towards the future goal of near real-time classification with an individually trained decoder, the minimum duration of training data necessary for successful classification was determined by using a chronological cross-validation approach. Main results. Close replication of the previously reported results confirmed the method robustness. Decoder performance remained stable from 96 channels down to 25. Furthermore, for less than 15 min of training data, the subject-independent (pre-trained) decoder performed better than an individually trained decoder did. Significance. Our study complements previous research and provides information suggesting that efficient low-density EEG online decoding is within reach.

Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single channel processing is not computationally efficient in compressed sensing of multi-channel EEG signals. Methods: An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low rank structures in the reconstructed multi-channel EEG signals. Both convex relaxation and global consensus optimization with alternating direction method of multipliers are used to compute the optimization model. Results: The performance of multi-channel EEG signal reconstruction is improved in term of both accuracy and computational complexity. Conclusion: The proposed method is a better candidate than previous sparse signal recovery methods for compressed sensing of EEG signals. Significance: The proposed method enables successful compressed sensing of EEG signals even when the signals have no good sparse representation. Using compressed sensing would much reduce the power consumption of wireless EEG system., Comment: 11 pages, 3 figures; accepted by IEEE Transactions on Biomedical Engineering

There is converging evidence that the auditory cortex takes over visual functions during a period of auditory deprivation. A residual pattern of cross-modal take-over may prevent the auditory cortex to adapt to restored sensory input as delivered by a cochlear implant (CI) and limit speech intelligibility with a CI. The aim of the present study was to investigate whether visual face processing in CI users activates auditory cortex and whether this has adaptive or maladaptive consequences. High-density electroencephalogram data were recorded from CI users (n=21) and age-matched normal hearing controls (n=21) performing a face versus house discrimination task. Lip reading and face recognition abilities were measured as well as speech intelligibility. Evaluation of event-related potential (ERP) topographies revealed significant group differences over occipito-temporal scalp regions. Distributed source analysis identified significantly higher activation in the right auditory cortex for CI users compared to NH controls, confirming visual take-over. Lip reading skills were significantly enhanced in the CI group and appeared to be particularly better after a longer duration of deafness, while face recognition was not significantly different between groups. However, auditory cortex activation in CI users was positively related to face recognition abilities. Our results confirm a cross-modal reorganization for ecologically valid visual stimuli in CI users. Furthermore, they suggest that residual takeover, which can persist even after adaptation to a CI is not necessarily maladaptive.

The mental practice of movements has been suggested as a promising add-on therapy to facilitate motor recovery after stroke. In the case of mentally practised movements, electroencephalogram (EEG) can be utilized to provide feedback about an otherwise covert act. The main target group for such an intervention are elderly patients, though research so far is largely focused on young populations (30 years). The present study therefore aimed to examine the influence of age on the neural correlates of covert movements (CMs) in a real-time EEG neurofeedback framework. CM-induced event-related desynchronization (ERD) was studied in young (mean age: 23.6 years) and elderly (mean age: 62.7 years) healthy adults. Participants performed covert and overt hand movements. CMs were based on kinesthetic motor imagery (MI) or quasi-movements (QM). Based on previous studies investigating QM in the mu frequency range (8-13Hz) QM were expected to result in more lateralized ERD% patterns and accordingly higher classification accuracies. Independent of CM strategy the elderly were characterized by a significantly reduced lateralization of ERD%, due to stronger ipsilateral ERD%, and in consequence, reduced classification accuracies. QM were generally perceived as more vivid, but no differences were evident between MI and QM in ERD% or classification accuracies. EEG feedback enhanced task-related activity independently of strategy and age. ERD% measures of overt and covert movements were strongly related in young adults, whereas in the elderly ERD% lateralization is dissociated. In summary, we did not find evidence in support of more pronounced ERD% lateralization patterns in QM. Our finding of a less lateralized activation pattern in the elderly is in accordance to previous research and with the idea that compensatory processes help to overcome neurodegenerative changes related to normal ageing. Importantly, it indicates that EEG neurofeedback studies should place more emphasis on the age of the potential end-users.

The attentional blink (AB) is a deficit in conscious perception of the second of two targets if it follows the first within 200–500 msec. The AB phenomenon has been linked to pre-target oscillatory alpha activity. However, this is based on paradigms that use a rapid serial visual presentation (RSVP) stimulus stream in which the targets are embedded. This distracter stream is usually presented at a frequency of 10 Hz and thus generates a steady-state visual-evoked potential (ssVEP) at the center of the alpha frequency band. This makes the interpretation of alpha findings in the AB difficult. To be able to relate these findings either to the presence of the ssVEP or to an effect of endogenously generated alpha activity, we compared AB paradigms with and without different pre-target distracter streams. The distracter stream was always presented at 12 Hz, and power and intertrial phase coherence were analyzed in the alpha range (8–12 Hz). Without a distracter stream alpha power dropped before target presentation, whereas coherence did not change. Presence of a distracter stream was linked to stronger pre-target power reduction and increased coherence, which were both modulated by distracter stream characteristics. With regard to the AB results indicated that, whereas ssVEP-related power tended to be higher when both targets were detected, endogenous alpha power tended to be lower. We argue that the pattern of results indicates that in the pre-target interval several processes act in parallel. The balance between these processes relates to the occurrence of an AB.

Simultaneous EEG-fMRI has proven to be useful in localizing interictal epileptic activity. However, the applicability of traditional GLM-based analysis is limited as interictal spikes are often not seen on the EEG inside the scanner. Therefore, we aim at extracting epileptic activity purely from the fMRI time series using independent component analysis (ICA). To our knowledge, we show for the first time that ICA can find sources related to epileptic activity in patients where no interictal spikes were recorded in the EEG. The epileptic components were identified retrospectively based on the known localization of the ictal onset zone (IOZ). We demonstrate that the selected components truly correspond to epileptic activity, as sources extracted from patients resemble significantly better the IOZ than sources found in healthy controls. Furthermore, we show that the epileptic components in patients with and without spikes recorded inside the scanner resemble the IOZ in the same degree. We conclude that ICA of fMRI has the potential to extend the applicability of EEG-fMRI for presurgical evaluation in epilepsy. ispartof: PLoS One vol:8 issue:11 pages:1-9 ispartof: location:United States status: published

In the past decade, technological advances have made it possible to reliably measure brain activity using simultaneous EEG-fMRI recordings inside an MR scanner. The main challenge then became to investigate the coupling between the EEG and fMRI signals in order to benefit from the simultaneously integrated temporal and spatial resolution. Although it is crucial to know when features in EEG and fMRI are expected to correlate with each other before the identification of common sources from multimodal data is possible, it is still a matter of debate. In this study, we address this question by analysing EEG and fMRI data separately from a face processing task. We show that we are able to reliably estimate single trial (ST) dynamics of face processing in EEG and fMRI data separately in four subjects. However, no correlation is found between the modalities. This implies that in this task modality-specific information is larger than the information that is shared by the modalities. ispartof: pages:6027-6030 ispartof: Proc. of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society vol:2013 pages:6027-6030 ispartof: 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC) location:Osaka, Japan date:3 Jul - 7 Jul 2013 status: published

Objective EEG is a valuable tool for evaluation of brain maturation in preterm babies. Preterm EEG constitutes of high voltage burst activities and more suppressed episodes, called interburst intervals (IBIs). Evolution of background characteristics provides information on brain maturation and helps in prediction of neurological outcome. The aim is to develop a method for automated burst detection. Methods Thirteen polysomnography recordings were used, collected at preterm postmenstrual age of 31.4 (26.1–34.4) weeks. We developed a burst detection algorithm based on the feature line length and compared it with manual scorings of clinical experts and other published methods. Results The line length–based algorithm is robust (84.27% accuracy, 84.00% sensitivity, 85.70% specificity). It is not critically dependent on the number of measurement channels, because two channels still provide 82% accuracy. Furthermore, it approximates well clinically relevant features, such as median IBI duration 5.45 (4.00–7.11) s, maximum IBI duration 14.02 (8.73–18.80) s and burst percentage 48.89 (35.45–60.12)%, with a median deviation of respectively 0.65 s, 1.96 s and 6.55%. Conclusion Automated assessment of long-term preterm EEG is possible and its use will optimize EEG interpretation in the NICU. Significance This study takes a first step towards fully automatic analysis of the preterm brain.

Neuroticism and negative affect have been associated with an increase in attentional investment and the greater processing of irrelevant stimuli. Previous research proposes the overinvestment of attention and a focused mental state as the mechanism of this effect. We investigated the neural correlates of this idea using a dual-stream rapid serial visual presentation paradigm with centrally presented, overlapping streams of letters that changed at different frequencies. Participants attended one stream at a time. We predicted that the more focused cognitive style associated with higher neuroticism would be reflected in the overinvestment of attention in the irrelevant stream of to-be-ignored letters, in particular, when the ignored stream was the more salient one. This was expected to lead to a smaller difference in power between the attended and unattended frequencies. Results showed that power differences between attended and unattended streams were negatively correlated with neuroticism scores in direct support of our hypothesis. Exploratory correlations also showed that extraversion was positively related to the attention difference. As extraversion has been contrasted to neuroticism and linked to increased cognitive flexibility and control in previous studies, it is possible that this trait may help in disengagement from salient stimuli. Together, these results provide the first neural correlates of the focused cognitive style idea. That the effect of extraversion is seen in the centro-parietal region and the effect of neuroticism is seen in the occipital region, indicate that these personality traits may affect the hierarchy of visual information processing. These findings provide new insight into the influence of personality traits on attention mechanisms and open up questions regarding the relationship between neuroticism, extraversion and information processing.

Multimodal approaches to brain imaging are getting increasingly popular among the neuroscience comunity. One such multimodal approach is the combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). In this paper we demonstrate two EEG-fMRI integration methods for contour integration task. First, we derrive the contour-selectivity measures from event related potential (ERP) and fMRI data, and explore the correlation between the two. In this way, we connect the spatial information from fMRI with the temporal information from ERPs. Thereafter, the results from this approach are compare to JointICA integration approach [5], [6], which aims at extracting spatio-temporal independent components, which are the combination of ERP and fMRI activations. ispartof: pages:6019-6022 ispartof: Proc. of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society vol:2013 pages:6019-6022 ispartof: 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC) location:Osaka, Japan date:3 Jul - 7 Jul 2013 status: published

In a previous study we presented a low-cost, small, and wireless 14-channel EEG system suitable for field recordings (Debener et al., 2012, psychophysiology). In the present follow-up study we investigated whether a single-trial P300 response can be reliably measured with this system, while subjects freely walk outdoors. Twenty healthy participants performed a three-class auditory oddball task, which included rare target and non-target distractor stimuli presented with equal probabilities of 16%. Data were recorded in a seated (control condition) and in a walking condition, both of which were realized outdoors. A significantly larger P300 event-related potential amplitude was evident for targets compared to distractors (p.001), but no significant interaction with recording condition emerged. P300 single-trial analysis was performed with regularized stepwise linear discriminant analysis and revealed above chance-level classification accuracies for most participants (19 out of 20 for the seated, 16 out of 20 for the walking condition), with mean classification accuracies of 71% (seated) and 64% (walking). Moreover, the resulting information transfer rates for the seated and walking conditions were comparable to a recently published laboratory auditory brain-computer interface (BCI) study. This leads us to conclude that a truly mobile auditory BCI system is feasible.

To build a low-cost, small, and wireless electroencephalogram (EEG) system suitable for field recordings, we merged consumer EEG hardware with an EEG electrode cap. Auditory oddball data were obtained while participants walked outdoors on university campus. Single-trial P300 classification with linear discriminant analysis revealed high classification accuracies for both indoor (77%) and outdoor (69%) recording conditions. We conclude that good quality, single-trial EEG data suitable for mobile brain-computer interfaces can be obtained with affordable hardware.

EEG inter-burst interval (IBI) and its evolution is a robust parameter for grading hypoxic encephalopathy and prognostication in newborns with perinatal asphyxia. We present a reliable algorithm for the automatic detection of IBIs. This automated approach is based on adaptive segmentation of EEG, classification of segments and use of temporal profiles to describe the global distribution of EEG activity. A pediatric neurologist has blindly scored data from 8 newborns with perinatal postasphyxial encephalopathy varying from mild to severe. 15 minutes of EEG have been scored per patient, thus totaling 2 hours of EEG that was used for validation. The algorithm shows good detection accuracy and provides insight into challenging cases that are difficult to detect.

PLoS one 7(12), e52267 (2012). doi:10.1371/journal.pone.0052267, Published by PLoS [u.a.], Lawrence, Kan.

The extraction of task-related single trial ERP features has recently gained much interest, in particular in simultaneous EEG-fMRI applications. In this study, a specific decomposition known as parallel factor analysis (PARAFAC) was used, in order to retrieve the task-related activity from the raw signals. Using visual detection task data, acquired in normal circumstances and simultaneously with fMRI, differences between distinct task-related conditions can be captured in the trial signatures of specific PARAFAC components when applied to ERP data arranged in Channels × Time × Trials arrays, but the signatures did not correlate with the fMRI data. Despite the need for parameter tuning and careful preprocessing, the approach is shown to be successful, especially when prior knowledge about the expected ERPs is incorporated.

Even though it is known that neonatal seizures are associated with acute brain lesions, the relationship of electroencephalographic (EEG) seizures to acute perinatal brain lesions visible on magnetic resonance imaging (MRI) has not been objectively studied. EEG source localization is successfully used for this purpose in adults, but it has not been sufficiently explored in neonates. Therefore, we developed an integrated method for ictal EEG dipole source localization based on a realistic head model to investigate the utility of EEG source imaging in neonates with postasphyxial seizures. We describe here our method and compare the dipole seizure localization results with acute perinatal lesions seen on brain MRI in 10 full‐term infants with neonatal encephalopathy. Through experimental studies, we also explore the sensitivity of our method to the electrode positioning errors and the variations in neonatal skull geometry and conductivity. The localization results of 45 focal seizures from 10 neonates are compared with the visual analysis of EEG and MRI data, scored by expert physicians. In 9 of 10 neonates, dipole locations showed good relationship with MRI lesions and clinical data. Our experimental results also suggest that the variations in the used values for skull conductivity or thickness have little effect on the dipole localization, whereas inaccurate electrode positioning can reduce the accuracy of source estimates. The performance of our fused method indicates that ictal EEG source imaging is feasible in neonates and with further validation studies, this technique can become a useful diagnostic tool. Hum Brain Mapp 34:2402–2417, 2013. © 2012 Wiley Periodicals, Inc.

Research on the neural basis of language processing has often avoided investigating spoken language production by fear of the electromyographic (EMG) artifacts that articulation induces on the electro-encephalogram (EEG) signal. Indeed, such articulation artifacts are typically much larger than the brain signal of interest. Recently, a Blind Source Separation technique based on Canonical Correlation Analysis was proposed to separate tonic muscle artifacts from continuous EEG recordings in epilepsy. In this paper, we show how the same algorithm can be adapted to remove the short EMG bursts due to articulation on every trial. Several analyses indicate that this method accurately attenuates the muscle contamination on the EEG recordings, providing to the neurolinguistic community a powerful tool to investigate the brain processes at play during overt language production.

Objective. In a previous study, we presented a low-cost, small and wireless EEG system enabling the recording of single-trial P300 amplitudes in a truly mobile, outdoor walking condition (Debener et?al (2012 Psychophysiology 49 1449?53)). Small and wireless mobile EEG systems have substantial practical advantages as they allow for brain activity recordings in natural environments, but these systems may compromise the EEG signal quality. In this study, we aim to evaluate the EEG signal quality that can be obtained with the mobile system. Approach. We compared our mobile 14-channel EEG system with a state-of-the-art wired laboratory EEG system in a popular brain?computer interface (BCI) application. N = 13 individuals repeatedly performed a 6???6 matrix P300 spelling task. Between conditions, only the amplifier was changed, while electrode placement and electrode preparation, recording conditions, experimental stimulation and signal processing were identical. Main results. Analysis of training and testing accuracies and information transfer rate (ITR) revealed that the wireless mobile EEG amplifier performed as good as the wired laboratory EEG system. A very high correlation for testing ITR between both amplifiers was evident (r = 0.92). Moreover the P300 topographies and amplitudes were very similar for both devices, as reflected by high degrees of association (r > = 0.77). Significance. We conclude that efficient P300 spelling with a small, lightweight and quick to set up mobile EEG amplifier is possible. This technology facilitates the transfer of BCI applications from the laboratory to natural daily life environments, one of the key challenges in current BCI research.