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In the development of a brain-computer interface (BCI), some issues should be regarded in order to improve its reliability and performance. Perhaps, one of the most challenging issues is related to the high variability of the brain signals, which directly impacts the accuracy of the classification. In this sense, novel feature extraction techniques should be explored in order to select those able to face this variability. Furthermore, to improve the performance of the selected feature extraction technique, the parameters of the filter applied in the preprocessing stage need to be properly selected. Then, this work presents an analysis of the robustness of the fractal dimension as feature extraction technique under high variability of the EEG signals, particularly when the training data are recorded one day and the testing data are obtained on a different day. The results are compared with those obtained by an autoregressive model, which is a technique commonly used in BCI applications. Also, the effect of properly selecting the cutoff frequencies of the filter in the preprocessing stage is evaluated. This research is supported by several experiments carried out using a public data set from the BCI international competition, specifically data set 2a from BCIIC IV, related to motor tasks. By a statistical test, it is demonstrated that the performance achieved using the fractal dimension is significantly better than that reached by the AR model. Also, it is demonstrated that the selection of the appropriate cutoff frequencies improves significantly the performance in the classification. The increase rate is approximately of 17%. [ABSTRACT FROM AUTHOR]

Background: When an unexpected perturbation in the environment occurs, the subsequent alertness state may cause a brain activation responding to that perturbation which can be detected and employed by a Brain-Computer Interface (BCI). In this work, the possibility of detecting a sudden obstacle appearance analyzing electroencephalographic (EEG) signals is assessed. For this purpose, different features of EEG signals are evaluated during the appearance of sudden obstacles while a subject is walking on a treadmill. The future goal is to use this procedure to detect any obstacle appearance during walking when the user is wearing a lower limb exoskeleton in order to generate an emergency stop command for the exoskeleton. This would enhance the user-exoskeleton interaction, improving the safety mechanisms of current exoskeletons.Methods: In order to detect the change in the brain activity when an obstacle suddenly appears, different features of EEG signals are evaluated using the recordings of five healthy subjects. Since the change in the brain activity occurs in the time domain, the features evaluated are: common spatial patterns, average power, slope, and the coefficients of a polynomial fit. A Linear Discriminant Analysis-based classifier is used to differentiate between two conditions: the appearance or not of an obstacle. The evaluation of the performance to detect the obstacles is made in terms of accuracy, true positive (TP) and false positive (FP) rates.Results: From the offline analysis, the best performance is achieved when the slope or the polynomial coefficients are used as features, with average detection accuracy rates of 74.0 and 79.5 %, respectively. These results are consistent with the pseudo-online results, where a complete EEG recording is segmented into windows of 500 ms and overlapped 400 ms, and a decision about the obstacle appearance is made for each window. The results of the best subject were 11 out of 14 obstacles detected with a rate of 9.09 FPs/min, and 10 out of 14 obstacles detected with a rate of 6.34 FPs/min using slope and polynomial coefficients features, respectively.Conclusions: An EEG-based BCI can be developed to detect the appearance of unexpected obstacles. The average accuracy achieved is 79.5 % of success rate with a low number of false detections. Thus, the online performance of the BCI would be suitable for commanding in a safely way a lower limb exoskeleton during walking. [ABSTRACT FROM AUTHOR]

The Neotropical ecoregion has been an important place of avian diversification where dispersal and allopatric events coupled with periods of active orogeny and climate change (Late Pliocene–Pleistocene) have shaped the biogeography of the region. In the Neotropics, avian population structure has been sculpted not only by geographical barriers, but also by non-allopatric factors such as natural selection and local adaptation. We analyzed the genetic variation of six co-distributed Phrygilus species from the Central Andes, based on mitochondrial and nuclear markers in conjunction with morphological differentiation. We examined if Phrygilus species share patterns of population structure and historical demography, and reviewed the intraspecific taxonomy in part of their geographic range. Our results showed different phylogeographic patterns between species, even among those belonging to the same phylogenetic clade. P. alaudinus , P. atriceps , and P. unicolor showed genetic differentiation mediated by allopatric mechanisms in response to specific geographic barriers; P. gayi showed sympatric lineages in northern Chile, while P. plebejus and P. fruticeti showed a single genetic group. We found no relationship between geographic range size and genetic structure. Additionally, a signature of expansion was found in three species related to the expansion of paleolakes in the Altiplano region and the drying phase of the Atacama Desert. Morphological analysis showed congruence with molecular data and intraspecific taxonomy in most species. While we detected genetic and phenotypic patterns that could be related to natural selection and local adaptation, our results indicate that allopatric events acted as a major factor in the population differentiation of Phrygilus species. [ABSTRACT FROM AUTHOR]

• Analysis of PCM melting in microgravity with the thermocapillary effect in liquid bridge geometry. • The phase change is studied by varying Marangoni and Stefan numbers, and Γ and dimensionless volume. • Results in cylindrical geometry demonstrate an improvement of a 50% with respect to rectangular geometry. • Extended analysis to (stable) non-cylindrical configurations. A detailed numerical investigation of the melting of phase change materials (PCMs) in microgravity is presented. We consider the high Prandtl number alkane n-octadecane and quantify the effect of thermocapillary convection on the heat transfer observed during axisymmetric melting with a liquid bridge geometry. The phase change is studied by varying key dimensionless parameters including Marangoni (Ma) and Stefan (Ste) numbers, which are selected by the applied temperature difference between the circular supports, and geometry, which is characterised by the aspect ratio Γ = L / R and the dimensionless volume V. The case of cylindrical geometry with V = 1 is considered first, and detailed analyses of the effect of both Ma (Ste) and Γ on heat transport and the type of oscillatory flow that appears in the liquid phase are provided. The results are compared with those available in the literature for rectangular geometry, demonstrating an improvement of approximately 50% for the cylindrical case. We further extend the analysis to noncylindrical configurations with V ≠ 1 that correspond to stable liquid bridges when fully melted. [ABSTRACT FROM AUTHOR]

An abstract of the study "Ionic currents and action potential induced by electrical stimulation, ACh and ATP in petrosal ganglion neurons," by R. Varas and colleagues is presented.