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Dictatorships that use plebiscites – that is, referendums initiated by the executive – have a longer expected lifespan. A successful plebiscite sends a signal that makes coordination for collective action more difficult and induces the falsification of preferences. It also enhances the status of the dictator within the regime and against potential rivals through the use of agenda power. As a result, plebiscites are followed by decreased mobilization and a reduced risk of palace coups. This, in turn, adds years to the dictator's tenure. We have found evidence to support these propositions by utilizing data from various databases of autocratic regimes that span from 1946 to 2008. [ABSTRACT FROM AUTHOR]

Background and objective. Motor imagery (MI) based brain-computer interfaces (BCIs) are widely used in rehabilitation due to the close relationship that exists between MI and motor execution (ME). However, the underlying brain mechanisms of MI remain not well understood. Most MI-BCIs use the sensorimotor rhythms elicited in the primary motor cortex (M1) and somatosensory cortex (S1), which consist of an event-related desynchronization followed by an event-related synchronization. Consequently, this has resulted in systems that only record signals around M1 and S1. However, MI could involve a more complex network including sensory, association, and motor areas. In this study, we hypothesize that the superior accuracies achieved by new deep learning (DL) models applied to MI decoding rely on focusing on a broader MI activation of the brain. Parallel to the success of DL, the field of explainable artificial intelligence (XAI) has seen continuous development to provide explanations for DL networks success. The goal of this study is to use XAI in combination with DL to extract information about MI brain activation patterns from non-invasive electroencephalography (EEG) signals. Methods. We applied an adaptation of Shapley additive explanations (SHAP) to EEGSym , a state-of-the-art DL network with exceptional transfer learning capabilities for inter-subject MI classification. We obtained the SHAP values from two public databases comprising 171 users generating left and right hand MI instances with and without real-time feedback. Results. We found that EEGSym based most of its prediction on the signal of the frontal electrodes, i.e. F7 and F8, and on the first 1500 ms of the analyzed imagination period. We also found that MI involves a broad network not only based on M1 and S1, but also on the prefrontal cortex (PFC) and the posterior parietal cortex (PPC). We further applied this knowledge to select a 8-electrode configuration that reached inter-subject accuracies of 86.5% ± 10.6% on the Physionet dataset and 88.7% ± 7.0% on the Carnegie Mellon University's dataset. Conclusion. Our results demonstrate the potential of combining DL and SHAP-based XAI to unravel the brain network involved in producing MI. Furthermore, SHAP values can optimize the requirements for out-of-laboratory BCI applications involving real users. • MI-BCIs aid rehabilitation, but underlying brain mechanisms are not well understood. • XAI + DL extracts MI brain activation patterns from EEG signals. • SHAP adaptation applied to state-of-the-art DL network, EEGSym. • SHAP based channel selection method obtains high inter-subject accuracies. • DL + SHAP-based XAI unveils brain network involved in MI optimizing BCI applications. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this work is the study of micelle formation in a solvent other than water, the ethyl-ammonium nitrate (EAN) (an ionic liquid at room temperature). The study of micelle formation will provide us with a better understanding of the interactions between the solvent and carbon chains. The critical micelle concentrations (cmc) for four different alkyl-ammonium nitrates have been experimentally determined. The method used for the determination of the cmc is based on surface tension measurements. In addition, the effect of micelle formation on molar partial volumes was checked. No volume augmentation was found in ethyl-ammonium nitrate. A theoretical model for the chemical potential of surfactants in ethyl-ammonium nitrate has also been proposed. This model served to explain the experimental results which were obtained. [Copyright &y& Elsevier]

Code-modulated visual evoked potentials (c-VEPs) have marked a milestone in the scientific literature due to their ability to achieve reliable, high-speed brain–computer interfaces (BCIs) for communication and control. Generally, these expert systems rely on encoding each command with shifted versions of binary pseudorandom sequences, i.e., flashing black and white targets according to the shifted code. Despite the excellent results in terms of accuracy and selection time, these high-contrast stimuli cause eyestrain for some users. In this work, we propose the use of non-binary p -ary m-sequences, whose levels are encoded with different shades of gray, as a more pleasant alternative than traditional binary codes. The performance and visual fatigue of these p -ary m-sequences, as well as their ability to provide reliable c-VEP-based BCIs, are analyzed for the first time. Five different m-sequences were evaluated with 16 healthy participants, following the circular shifting paradigm: base 2 (63 bits), base 3 (80 bits), base 5 (124 bits), base 7 (48 bits), and base 11 (120 bits). Signal processing consisted of a 3-filter bank (1–60 Hz, 12–60 Hz and 30–60 Hz), followed by a canonical correlation analysis. Raster latency correction and artifact rejection approaches were also applied to compute command templates. For each m-sequence, users performed a 30-trial calibration stage, followed by an online spelling task of 32 trials. In addition, qualitative measures regarding visual fatigue and satisfaction were collected. Users were able to achieve an average accuracy of over 98% for all p -ary m-sequences. The differences between m-sequences were not significant in terms of accuracy, but they were in terms of visual fatigue. The higher the base, the less eyestrain perceived by users for both presentation rates of 60 Hz and 120 Hz. All p -ary m-sequences were also significantly less annoying when displayed at 120 Hz compared to 60 Hz. Results suggest that all p -ary m-sequences are suitable for achieving high speed and high accuracy in c-VEP-based BCIs, reducing the visual fatigue as the base increases, without degrading system performance. It is concluded that the use of high presentation rates and non-binary m-sequences is a promising alternative to provide user-friendly c-VEP-based BCIs. • Five p-ary m-sequences were used for the first time in a c-VEP-based BCI. • The higher the base of the m-sequence, the less eyestrain perceived by users. • All m-sequences were significantly less fatiguing at a 120 Hz compared to 60 Hz. • P-ary m-sequences can achieve high speed and high accuracy in BCIs based on c-VEP. • The use of p-ary m-sequences can improve user comfort in c-VEP-based BCIs. [ABSTRACT FROM AUTHOR]

For sensitive infrared interferometry, it is crucial to control the differential piston evolution between the used telescopes. This is classically done by the use of a fringe tracker. In this work, we develop a new method to reconstruct the temporal piston variation from the atmosphere, by using real-time data from adaptive optics (AO) wavefront sensing: the Piston Reconstruction Experiment (P-REx). In order to understand the principle performance of the system in a realistic multilayer atmosphere, it is first extensively tested in simulations. The gained insights are then used to apply P-REx to real data, in order to demonstrate the benefit of using P-REx as an auxiliary system in a real interferometer. All tests show positive results, which encourages further research and eventually a real implementation. Especially, the tests on on-sky data showed that the atmosphere is, under decent observing conditions, sufficiently well structured and stable, in order to apply P-REx. It was possible to conveniently reconstruct the piston evolution in two-thirds of the data sets from good observing conditions (r0 ~ 30 cm). The main conclusion is that applying the piston reconstruction in a real system would reduce the piston variation from around 10 m down to 1-2 m over time-scales of up to two seconds. This suggests an application for mid-infrared interferometry, for example for MATISSE at the very large telescope interferometer or the large binocular telescope interferometer. P-REx therefore provides the possibility to improve interferometric measurements without the need for more complex AO systems than already in regular use at 8-m-class telescopes. [ABSTRACT FROM AUTHOR]

• First method based on deep learning that provides robust asynchronous control of ERP-based spellers through monitoring of user attention. • Algorithm focused on practical BCI applications: modular system design and reduced calibration time taking advantage from transfer learning. • Comprehensive performance analysis in a database of 22 subjects, reaching a maximum average accuracy of 96.95% for control state detection. • The proposed method outperformed previous approaches, as shown in the comparative analysis. Background and Objective. Brain-computer interfaces (BCI) based on event-related potentials (ERP) are a promising technology for alternative and augmented communication in an assistive context. However, most approaches to date are synchronous, requiring the intervention of a supervisor when the user wishes to turn his attention away from the BCI system. In order to bring these BCIs into real-life applications, a robust asynchronous control of the system is required through monitoring of user attention. Despite the great importance of this limitation, which prevents the deployment of these systems outside the laboratory, it is often overlooked in research articles. This study was aimed to propose a novel method to solve this problem, taking advantage of deep learning for the first time in this context to overcome the limitations of previous strategies based on hand-crafted features. Methods. The proposed method, based on EEG-Inception, a novel deep convolutional neural network, divides the problem in 2 stages to achieve the asynchronous control: (i) the model detects user's control state, and (ii) decodes the command only if the user is attending to the stimuli. Additionally, we used transfer learning to reduce the calibration time, even exploring a calibration-less approach. Results. Our method was evaluated with 22 healthy subjects, analyzing the impact of the calibration time and number of stimulation sequences on the system's performance. For the control state detection stage, we report average accuracies above 91% using only 1 sequence of stimulation and 30 calibration trials, reaching a maximum of 96.95% with 15 sequences. Moreover, our calibration-less approach also achieved suitable results, with a maximum accuracy of 89.36%, showing the benefits of transfer learning. As for the overall asynchronous system, which includes both stages, the maximum information transfer rate was 35.54 bpm, a suitable value for high-speed communication. Conclusions. The proposed strategy achieved higher performance with less calibration trials and stimulation sequences than former approaches, representing a promising step forward that paves the way for more practical applications of ERP-based spellers. [ABSTRACT FROM AUTHOR]

• MEDUSA is a novel open-source, Python-based software ecosystem to accelerate BCI and cognitive neuroscience research. • Compatibility with any signal acquisition system through the lab-streaming layer (LSL) protocol, including the possibility of recording multiple signals at the same time. • Complete suite of BCI paradigms and cognitive neuroscience experiments: c-VEP) and P300 spellers, motor imagery, neurofeedback and multiple neuropsychological tasks. • State-of-the-art signal processing methods and models for offline and online analysis. • Developer tools to simplify the implementation and sharing of custom open-loop and closed-loop experiments for BCI and cognitive neuroscience. Background and objective: Neurotechnologies have great potential to transform our society in ways that are yet to be uncovered. The rate of development in this field has increased significantly in recent years, but there are still barriers that need to be overcome before bringing neurotechnologies to the general public. One of these barriers is the difficulty of performing experiments that require complex software, such as brain-computer interfaces (BCI) or cognitive neuroscience experiments. Current platforms have limitations in terms of functionality and flexibility to meet the needs of researchers, who often need to implement new experimentation settings. This work was aimed to propose a novel software ecosystem, called MEDUSA©, to overcome these limitations. Methods: We followed strict development practices to optimize MEDUSA© for research in BCI and cognitive neuroscience, making special emphasis in the modularity, flexibility and scalability of our solution. Moreover, it was implemented in Python, an open-source programming language that reduces the development cost by taking advantage from its high-level syntax and large number of community packages. Results: MEDUSA© provides a complete suite of signal processing functions, including several deep learning architectures or connectivity analysis, and ready-to-use BCI and neuroscience experiments, making it one of the most complete solutions nowadays. We also put special effort in providing tools to facilitate the development of custom experiments, which can be easily shared with the community through an app market available in our website to promote reproducibility. Conclusions: MEDUSA© is a novel software ecosystem for modern BCI and neurotechnology experimentation that provides state-of-the-art tools and encourages the participation of the community to make a difference for the progress of these fields. Visit the official website at https://www.medusabci.com/ to know more about this project. [ABSTRACT FROM AUTHOR]

Neurofeedback training (NFT) has shown promising results in recent years as a tool to address the effects of age-related cognitive decline in the elderly. Since previous studies have linked reduced complexity of electroencephalography (EEG) signal to the process of cognitive decline, we propose the use of non-linear methods to characterise changes in EEG complexity induced by NFT. In this study, we analyse the pre- and post-training EEG from 11 elderly subjects who performed an NFT based on motor imagery (MI–NFT). Spectral changes were studied using relative power (RP) from classical frequency bands (delta, theta, alpha, and beta), whilst multiscale entropy (MSE) was applied to assess EEG-induced complexity changes. Furthermore, we analysed the subject's scores from Luria tests performed before and after MI–NFT. We found that MI–NFT induced a power shift towards rapid frequencies, as well as an increase of EEG complexity in all channels, except for C3. These improvements were most evident in frontal channels. Moreover, results from cognitive tests showed significant enhancement in intellectual and memory functions. Therefore, our findings suggest the usefulness of MI–NFT to improve cognitive functions in the elderly and encourage future studies to use MSE as a metric to characterise EEG changes induced by MI–NFT. [ABSTRACT FROM AUTHOR]