Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX

This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 prevalent NN models and their variants across four brain-computer interface (BCI) paradigms, we gauged their information representation capability. Rooted in comprehensive literature review findings, we proposed EEGNeX, a novel, purely ConvNet-based architecture. We pitted it against both existing cutting-edge strategies and the Mother of All BCI Benchmarks (MOABB) involving 11 distinct EEG motor imagination (MI) classification tasks and revealed that EEGNeX surpasses other state-of-the-art methods. Notably, it shows up to 2.1%-8.5% improvement in the classification accuracy in different scenarios with statistical significance (p < 0.05) compared to its competitors. This study not only provides deeper insights into designing efficient NN models for EEG data but also lays groundwork for future explorations into the relationship between bioelectric brain signals and NN architectures. For the benefit of broader scientific collaboration, we have made all benchmark models, including EEGNeX, publicly available at (https://github.com/chenxiachan/EEGNeX).Comment: 19 pages, 6 figure

Dual brain stimulation enhances interpersonal learning through spontaneous movement synchrony

Abstract Social interactive learning denotes the ability to acquire new information from a conspecific—a prerequisite for cultural evolution and survival. As inspired by recent neurophysiological research, here we tested whether social interactive learning can be augmented by exogenously synchronizing oscillatory brain activity across an instructor and a learner engaged in a naturalistic song-learning task. We used a dual brain stimulation protocol entailing the trans-cranial delivery of synchronized electric currents in two individuals simultaneously. When we stimulated inferior frontal brain regions, with 6 Hz alternating currents being in-phase between the instructor and the learner, the dyad exhibited spontaneous and synchronized body movement. Remarkably, this stimulation also led to enhanced learning performance. These effects were both phase- and frequency-specific: 6 Hz anti-phase stimulation or 10 Hz in-phase stimulation, did not yield comparable results. Furthermore, a mediation analysis disclosed that interpersonal movement synchrony acted as a partial mediator of the effect of dual brain stimulation on learning performance, i.e. possibly facilitating the effect of dual brain stimulation on learning. Our results provide a causal demonstration that inter-brain synchronization is a sufficient condition to improve real-time information transfer between pairs of individuals

Characteristics of Dissolved Organic Matter in a Semi-closed Bay in Summer: Insights from Stable Isotope and Optical Analyses

Dissolved organic matter (DOM) serves as the most active and sensitive organic component in the bay, and its biogeochemical characteristics and reactivity are affected by the properties of terrestrial and marine substances significantly. In this study, in order to study the distribution and characteristics of DOM in a semi-closed bay, 34 water samples from 19 stations were collected from Zhanjiang Bay and analyzed for δ13C of dissolved inorganic carbon (DIC) and fluorescent components of DOM. The results showed that there were many sources of organic matter in the bay, including soil input, algae input, and sewage input. Influenced by freshwater input, DOM in the bay decreased from the upper bay to the outer bay. The organic matter in the bay displayed two characteristics, where the northern bay is composed of terrigenous organic matter mainly with high humus, while the southern bay is more inclined to marine sources with a high biological index (BIX) and low humification index (HIX). The correlation between organic matter with different characteristics and environmental parameters such as salinity, pH, and chlorophyll a was analyzed. The discrepancy may be caused by the weak turbulent mixing in the semi-closed bay

Comparative Genomics Study of Multi-Drug-Resistance Mechanisms in the Antibiotic-Resistant Streptococcus suis R61 Strain

BACKGROUND: Streptococcus suis infections are a serious problem for both humans and pigs worldwide. The emergence and increasing prevalence of antibiotic-resistant S. suis strains pose significant clinical and societal challenges. RESULTS: In our study, we sequenced one multi-drug-resistant S. suis strain, R61, and one S. suis strain, A7, which is fully sensitive to all tested antibiotics. Comparative genomic analysis revealed that the R61 strain is phylogenetically distinct from other S. suis strains, and the genome of R61 exhibits extreme levels of evolutionary plasticity with high levels of gene gain and loss. Our results indicate that the multi-drug-resistant strain R61 has evolved three main categories of resistance. CONCLUSIONS: Comparative genomic analysis of S. suis strains with diverse drug-resistant phenotypes provided evidence that horizontal gene transfer is an important evolutionary force in shaping the genome of multi-drug-resistant strain R61. In this study, we discovered novel and previously unexamined mutations that are strong candidates for conferring drug resistance. We believe that these mutations will provide crucial clues for designing new drugs against this pathogen. In addition, our work provides a clear demonstration that the use of drugs has driven the emergence of the multi-drug-resistant strain R61

National identity predicts public health support during a global pandemic

Changing collective behaviour and supporting non-pharmaceutical interventions is an important component in mitigating virus transmission during a pandemic. In a large international collaboration (Study 1, N = 49,968 across 67 countries), we investigated self-reported factors associated with public health behaviours (e.g., spatial distancing and stricter hygiene) and endorsed public policy interventions (e.g., closing bars and restaurants) during the early stage of the COVID-19 pandemic (April-May 2020). Respondents who reported identifying more strongly with their nation consistently reported greater engagement in public health behaviours and support for public health policies. Results were similar for representative and non-representative national samples. Study 2 (N = 42 countries) conceptually replicated the central finding using aggregate indices of national identity (obtained using the World Values Survey) and a measure of actual behaviour change during the pandemic (obtained from Google mobility reports). Higher levels of national identification prior to the pandemic predicted lower mobility during the early stage of the pandemic (r = −0.40). We discuss the potential implications of links between national identity, leadership, and public health for managing COVID-19 and future pandemics.publishedVersio

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution—individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.Peer reviewe

National identity predicts public health support during a global pandemic (vol 13, 517, 2022) : National identity predicts public health support during a global pandemic (Nature Communications, (2022), 13, 1, (517), 10.1038/s41467-021-27668-9)

Publisher Copyright: © The Author(s) 2022.In this article the author name ‘Agustin Ibanez’ was incorrectly written as ‘Augustin Ibanez’. The original article has been corrected.Peer reviewe