An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts.

This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of T1 and T2-properness, which entails a reduction in the dimension of the problem and, consequently, computational savings. The methodology proposed enables us to provide an optimal (in the least-mean-squares sense) linear fusion filtering algorithm for estimating the tessarine state with a lower computational cost than the conventional one devised in the real field. Simulation results illustrate the performance and advantages of the solution proposed in different settings.

Magnetic Field Mapping and Correction for Moving OP-MEG.

BACKGROUND: Optically pumped magnetometers (OPMs) have made moving, wearable magnetoencephalography (MEG) possible. The OPMs typically used for MEG require a low background magnetic field to operate, which is achieved using both passive and active magnetic shielding. However, the background magnetic field is never truly zero Tesla, and so the field at each of the OPMs changes as the participant moves. This leads to position and orientation dependent changes in the measurements, which manifest as low frequency artefacts in MEG data. OBJECTIVE: We model the spatial variation in the magnetic field and use the model to predict the movement artefact found in a dataset. METHODS: We demonstrate a method for modelling this field with a triaxial magnetometer, then show that we can use the same technique to predict the movement artefact in a real OPM-based MEG (OP-MEG) dataset. RESULTS: Using an 86-channel OP-MEG system, we found that this modelling method maximally reduced the power spectral density of the data by 27.8 ± 0.6 dB at 0 Hz, when applied over 5 s non-overlapping windows. CONCLUSION: The magnetic field inside our state-of-the art magnetically shielded room can be well described by low-order spherical harmonic functions. We achieved a large reduction in movement noise when we applied this model to OP-MEG data. SIGNIFICANCE: Real-time implementation of this method could reduce passive shielding requirements for OP-MEG recording and allow the measurement of low-frequency brain activity during natural participant movement.

Data-driven model optimization for optically pumped magnetometer sensor arrays.

Optically pumped magnetometers (OPMs) have reached sensitivity levels that make them viable portable alternatives to traditional superconducting technology for magnetoencephalography (MEG). OPMs do not require cryogenic cooling and can therefore be placed directly on the scalp surface. Unlike cryogenic systems, based on a well-characterised fixed arrays essentially linear in applied flux, OPM devices, based on different physical principles, present new modelling challenges. Here, we outline an empirical Bayesian framework that can be used to compare between and optimise sensor arrays. We perturb the sensor geometry (via simulation) and with analytic model comparison methods estimate the true sensor geometry. The width of these perturbation curves allows us to compare different MEG systems. We test this technique using simulated and real data from SQUID and OPM recordings using head-casts and scanner-casts. Finally, we show that given knowledge of underlying brain anatomy, it is possible to estimate the true sensor geometry from the OPM data themselves using a model comparison framework. This implies that the requirement for accurate knowledge of the sensor positions and orientations a priori may be relaxed. As this procedure uses the cortical manifold as spatial support there is no co-registration procedure or reliance on scalp landmarks.

Atypical Modulations of N170 Component during Emotional Processing and Their Links to Social Behaviors in Ex-combatants.

Emotional processing (EP) is crucial for the elaboration and implementation of adaptive social strategies. EP is also necessary for the expression of social cognition and behavior (SCB) patterns. It is well-known that war contexts induce socio-emotional atypical functioning, in particular for those who participate in combats. Thus, ex-combatants represent an ideal non-clinical population to explore EP modulation and to evaluate its relation with SCB. The aim of this study was to explore EP and its relation with SCB dimensions such as empathy, theory of mind and social skills in a sample of 50 subjects, of which 30 were ex-combatants from illegally armed groups in Colombia, and 20 controls without combat experience. We adapted an Emotional Recognition Task for faces and words and synchronized it with electroencephalographic recording. Ex-combatants presented with higher assertion skills and showed more pronounced brain responses to faces than Controls. They did not show the bias toward anger observed in control participants whereby the latter group was more likely to misclassify neutral faces as angry. However, ex-combatants showed an atypical word valence processing. That is, words with different emotions yielded no differences in N170 modulations. SCB variables were successfully predicted by neurocognitive variables. Our results suggest that in ex-combatants the links between EP and SCB functions are reorganized. This may reflect neurocognitive modulations associated to chronic exposure to war experiences.

Social Cognitive Training Improves Emotional Processing and Reduces Aggressive Attitudes in Ex-combatants.

Emotional processing (EP) is a complex cognitive function necessary to successfully adjust to social environments where we need to interpret and respond to cues that convey threat or reward signals. Ex-combatants have consistently shown atypical EP as well as poor social interactions. Available reintegration programs aim to facilitate the re-adaptation of ex-combatants to their communities. However, they do not incorporate actions to improve EP and to enhance cognitive-emotional regulation. The present study was aimed at evaluating the usefulness of an intervention focused on Social Cognitive Training (SCT), which was designed to equip ex-combatants enrolled in the Social Reintegration Route with EP and social cognition skills. A group of 31 ex-combatants (mean age of 37.2, 29 men) from Colombian illegal armed groups were recruited into this study. Of these, 16 were invited to take part in a SCT and the other continued with the conventional reintegration intervention. Both groups underwent 12 training sessions in a period 12-14 weeks. They were assessed with a comprehensive protocol which included Psychosocial, Behavioral, and Emotion Processing instruments. The scores on these instruments prior to and after the intervention were compared within and between groups. Both groups were matched at baseline. Ex-combatants receiving the SCT experienced significant improvements in EP and a reduction in aggressive attitudes, effects not observed in those continuing the conventional reintegration intervention. This is the first study that achieves such outcomes in such a population using SCT intervention. We discuss the implications of such results toward better social reintegration strategies.

Non-linear Parameter Estimates from Non-stationary MEG Data.

We demonstrate a method to estimate key electrophysiological parameters from resting state data. In this paper, we focus on the estimation of head-position parameters. The recovery of these parameters is especially challenging as they are non-linearly related to the measured field. In order to do this we use an empirical Bayesian scheme to estimate the cortical current distribution due to a range of laterally shifted head-models. We compare different methods of approaching this problem from the division of M/EEG data into stationary sections and performing separate source inversions, to explaining all of the M/EEG data with a single inversion. We demonstrate this through estimation of head position in both simulated and empirical resting state MEG data collected using a head-cast.

Two-threshold energy based fall detection using a triaxial accelerometer.

Elderly fall detection based on accelerometers is an active research area. Nowadays authors are addressing specific problems such as failure rates and energy consumption, but in most cases their strategies do not conciliate these objectives. In this paper we propose a double threshold based methodology with two novel detection features, a product between the sum vector magnitude and the signal magnitude area, and a normalization of the signal magnitude area over five 1 s windows. The methodology was validated using the public Mobifall dataset, and one developed for this work. It achieved 99 % of accuracy with Mobifall, and 97 % with the self-developed dataset. This methodology is based on an activity by activity analysis performed for determining which activities are prone to fail, as an alternative way of reducing detection failures.

Allergen sensitization linked to climate and age, not to intermittent-persistent rhinitis in a cross-sectional cohort study in the (sub)tropics.

BACKGROUND: Allergen exposure leads to allergen sensitization in susceptible individuals and this might influence allergic rhinitis (AR) phenotype expression. We investigated whether sensitization patterns vary in a country with subtropical and tropical regions and if sensitization patterns relate to AR phenotypes or age. METHODS: In a national, cross-sectional study AR patients (2-70 y) seen by allergists underwent blinded skin prick testing with a panel of 18 allergens and completed a validated questionnaire on AR phenotypes. RESULTS: 628 patients were recruited. The major sensitizing allergen was house dust mite (HDM) (56%), followed by Bermuda grass (26%), ash (24%), oak (23%) and mesquite (21%) pollen, cat (22%) and cockroach (21%). Patients living in the tropical region were almost exclusively sensitized to HDM (87%). In the central agricultural zones sensitization is primarily to grass and tree pollen. Nationwide, most study subjects had perennial (82.2%), intermittent (56.5%) and moderate-severe (84.7%) AR. Sensitization was not related to the intermittent-persistent AR classification or to AR severity; seasonal AR was associated with tree (p < 0.05) and grass pollen sensitization (p < 0.01). HDM sensitization was more frequent in children (0-11 y) and adolescents (12-17 y) (subtropical region: p < 0.0005; tropical region p < 0.05), but pollen sensitization becomes more important in the adult patients visiting allergists (Adults vs children + adolescents for tree pollen: p < 0.0001, weeds: p < 0.0005). CONCLUSIONS: In a country with (sub)tropical climate zones SPT sensitization patterns varied according to climatological zones; they were different from those found in Europe, HDM sensitization far outweighing pollen allergies and Bermuda grass and Ash pollen being the main grass and tree allergens, respectively. Pollen sensitization was related to SAR, but no relation between sensitization and intermittent-persistent AR or AR severity could be detected. Sensitization patterns vary with age (child HDM, adult pollen). Clinical implications of our findings are dual: only a few allergens -some region specific- cover the majority of sensitizations in (sub)tropical climate zones. This is of major importance for allergen manufacturers and immunotherapy planning. Secondly, patient selection in clinical trials should be based on the intermittent-persistent and severity classifications, rather than on the seasonal-perennial AR subtypes, especially when conducted in (sub)tropical countries.

Artificial Neural Networks as an alternative to traditional fall detection methods.

Falls are common events among older adults and may have serious consequences. Automatic fall detection systems are becoming a popular tool to rapidly detect such events, helping family or health personal to rapidly help the person that falls. This paper presents the results obtained in the process of testing a new fall detection method, based on Artificial Neural Networks (ANN). This method intends to improve fall detection accuracy, by avoiding the traditional threshold - based fall detection methods, and introducing ANN as a suitable option on this application.Also ANN have low computational cost, this characteristic makes them easy to implement on a portable device, comfortable to be wear by the patient.

Influence of isotropic skull models on EEG source localization.

We investigated the influence of using simplified skull models on electroencephalogram (EEG) source localization. The simplified skull models were derived from CT and MR images, with isotropic conductivity modeled as either heterogeneous or homogeneous. A total of four simplified head models were compared against a reference model with a skull accurately segmented with CT images. Our results show that the use of a simplified geometry for the skull, can lead to errors of approximately 1 cm for sources located in the central and temporal regions of the brain.

Cortical surface reconstruction based on MEG data and spherical harmonics.

Estimates of coefficients of a spherical harmonic Fourier decomposition of the cortical surface can be obtained solely using MEG/EEG data and free energy as objective function. A stochastic methodology based on a Metropolis Search followed by a Bayesian Model Averaging is proposed to reconstruct cortical anatomy based functional information.

Random location of multiple sparse priors for solving the MEG/EEG inverse problem.

MEG/EEG brain imaging has become an important tool in neuroimaging. Current techniques based in Bayesian approaches require an a-priori definition of patch locations on the cortical manifold. Too many patches results in a complex optimisation problem, too few an under sampling of the solution space. In this work random locations of the possible active regions of the brain are proposed to iteratively arrive at a solution. We use Bayesian model averaging to combine different possible solutions. The proposed methodology was tested with synthetic MEG datasets reducing the localisation error of the approaches based on fixed locations. Real data from a visual attention study was used for validation.