Gamma-ray bursts (GRBs) are divided into two populations1,2; long GRBs that derive from the core collapse of massive stars (for example, ref. 3) and short GRBs that form in the merger of two compact objects4,5. Although it is common to divide the two populations at a gamma-ray duration of 2 s, classification based on duration does not always map to the progenitor. Notably, GRBs with short (≲2 s) spikes of prompt gamma-ray emission followed by prolonged, spectrally softer extended emission (EE-SGRBs) have been suggested to arise from compact object mergers6–8. Compact object mergers are of great astrophysical importance as the only confirmed site of rapid neutron capture (r-process) nucleosynthesis, observed in the form of so-called kilonovae9–14. Here we report the discovery of a possible kilonova associated with the nearby (350 Mpc), minute-duration GRB 211211A. The kilonova implies that the progenitor is a compact object merger, suggesting that GRBs with long, complex light curves can be spawned from merger events. The kilonova of GRB 211211A has a similar luminosity, duration and colour to that which accompanied the gravitational wave (GW)-detected binary neutron star (BNS) merger GW170817 (ref. 4). Further searches for GW signals coincident with long GRBs are a promising route for future multi-messenger astronomy. A possible kilonova associated with a nearby, long-duration gamma-ray burst suggests that gamma-ray bursts with long and complex light curves can be spawned from the merger of two compact objects, contrary to the established gamma-ray burst paradigm. 

/pdf/a-kilonova-following-a-long-duration-gamma-ray-burst-at-350-14170hps.pdf

A kilonova following a long-duration gamma-ray burst at 350 Mpc

For decades, gamma-ray bursts (GRBs) have been broadly divided into `long'- and `short'-duration bursts, lasting more or less than 2s, respectively. However, this dichotomy does not map perfectly to the two progenitor channels that are known to produce GRBs -- the merger of compact objects (merger-GRBs) or the collapse of massive stars (collapsar-GRBs). In particular, the merger-GRBs population may also include bursts with a short, hard $\lesssim$2s spike and subsequent longer, softer extended emission (EE). The recent discovery of a kilonova -- the radioactive glow of heavy elements made in neutron star mergers -- in the 50s-duration GRB 211211A further demonstrates that mergers can drive long, complex GRBs that mimic the collapsar population. Here we present a detailed temporal and spectral analysis of the high-energy emission of GRB 211211A. We demonstrate that the emission has a purely synchrotron origin, with both the peak and cooling frequencies moving through the $\gamma$-ray band down to the X-rays, and that the rapidly-evolving spectrum drives the EE signature at late times. The identification of such spectral evolution in a merger-GRB opens avenues for diagnostics of the progenitor type. 

/pdf/the-case-for-a-minute-long-merger-driven-gamma-ray-burst-8llg4ndc.pdf

The case for a minute-long merger-driven gamma-ray burst from fast-cooling synchrotron emission

Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros–cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

/pdf/connectivity-analysis-in-eeg-data-a-tutorial-review-of-the-2pgko733.pdf

Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends

The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals’ complexity and non-stationarity. Additionally, the effects caused by individual variances may hamper the generalization of detection systems. Given the association between EEG signals and particular demographics, such as gender and age, and the influences of these demographic characteristics on the incidence of depression, it would be preferable to include demographic factors during EEG modeling and depression detection. The main objective of this work is to develop an algorithm that can recognize depression patterns by studying EEG data. Following a multiband analysis of such signals, machine learning and deep learning techniques were used to detect depression patients automatically. EEG signal data are collected from the multi-modal open dataset MODMA and employed in studying mental diseases. The EEG dataset contains information from a traditional 128-electrode elastic cap and a cutting-edge wearable 3-electrode EEG collector for widespread applications. In this project, resting EEG readings of 128 channels are considered. According to CNN, training with 25 epoch iterations had a 97% accuracy rate. The patient’s status has to be divided into two basic categories: major depressive disorder (MDD) and healthy control. Additional MDD include the following six classes: obsessive-compulsive disorders, addiction disorders, conditions brought on by trauma and stress, mood disorders, schizophrenia, and the anxiety disorders discussed in this paper are a few examples of mental illnesses. According to the study, a natural combination of EEG signals and demographic data is promising for the diagnosis of depression.

https://www.mdpi.com/2075-4418/13/10/1779/pdf?version=1684382439

Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques

Due to relativistic bulk motion, the structure and orientation of gamma-ray burst (GRB) jets have a fundamental role in determining how they appear. The recent discovery of the GW170817 binary neutron star merger and the associated GRB boosted the interest in the modeling and search for signatures of the presence of a (possibly quasi-universal) jet structure in long and short GRBs. In this review, following a pedagogical approach, we summarize the history of GRB jet structure research over the last two decades, from the inception of the idea of a universal jet structure to the current understanding of the complex processes that shape the structure, which involves the central engine that powers the jet and the interaction of the latter with the progenitor vestige. We put some emphasis on the observable imprints of jet structure on prompt and afterglow emission and on the luminosity function, favoring intuitive reasoning over technical explanations.

/pdf/the-structure-of-gamma-ray-burst-jets-2d03aenb.pdf

The Structure of Gamma Ray Burst Jets

Electroencephalogram channel selection based on pearson correlation coefficient for motor imagery-brain-computer interface

Machine learning techniques for electroencephalogram based brain-computer interface: A systematic literature review

Motor imagery signal classification using Wavelet packet decomposition and modified binary grey wolf optimization

An energetic γ -ray burst (GRB), GRB 211211A, was observed on 2021 December 11 by the Neil Gehrels Swift Observatory. Despite its long duration, typically associated with bursts produced by the collapse of massive stars, the discovery of an optical-infrared kilonova and a quasi-periodic oscillation during a gamma-ray precursor points to a compact ( ∼ 1 billion light-years) burst will signiﬁcantly impact our knowledge of GRB progenitors and the physical processes that lead to electromagnetic emission in compact binary mergers. Here, we report the discovery of a signiﬁcant ( > 5 σ ) transient-like emission in the high-energy γ -rays (HE; E > 0 . 1 GeV) observed by Fermi /LAT starting at 10 3 s after the burst. After an initial phase with a roughly constant ﬂux ( ∼ 5 × 10 − 10 erg s − 1 cm − 2 ) lasting ∼ 2 × 10 4 s, the ﬂux started decreasing and soon went undetected. The multi-wavelength ‘afterglow’ emission observed at such late times is usually in good agreement with synchrotron emission from a relativistic shock wave that arises as the GRB jet decelerates in the interstellar medium. However, our detailed modelling of a rich dataset comprising public and dedicated multi-wavelength observations demonstrates that GeV emission from GRB 211211A is in excess with respect to the expectation of this scenario. We explore the possibility that the GeV excess is inverse Compton emission due to the interaction of a long-lived, low-power jet with an external source of photons. We discover that the kilonova emission can provide the necessary seed photons for GeV emission in binary neutron star mergers.

GeV emission from a compact binary merger

Performance Estimation of Dual-Halo Dual-Material Silicon-On-Insulator with High-K Dielectrics for Low Power Applications

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