On the Usage of Linear Regression Models to Reconstruct Limb Kinematics from Low Frequency EEG Signals

Several works have reported on the reconstruction of 2D/3D limb kinematics from low-frequency EEG signals using linear regression models based on positive correlation values between the recorded and the reconstructed trajectories. This paper describes the mathematical properties of the linear model and the correlation evaluation metric that may lead to a misinterpretation of the results of this type of decoders. Firstly, the use of a linear regression model to adjust the two temporal signals (EEG and velocity profiles) implies that the relevant component of the signal used for decoding (EEG) has to be in the same frequency range as the signal to be decoded (velocity profiles). Secondly, the use of a correlation to evaluate the fitting of two trajectories could lead to overly-optimistic results as this metric is invariant to scale. Also, the correlation has a non-linear nature that leads to higher values for sinus/cosinus-like signals at low frequencies. Analysis of these properties on the reconstruction results was carried out through an experiment performed in line with previous studies, where healthy participants executed predefined reaching movements of the hand in 3D space. While the correlations of limb velocity profiles reconstructed from low-frequency EEG were comparable to studies in this domain, a systematic statistical analysis revealed that these results were not above the chance level. The empirical chance level was estimated using random assignments of recorded velocity profiles and EEG signals, as well as combinations of randomly generated synthetic EEG with recorded velocity profiles and recorded EEG with randomly generated synthetic velocity profiles. The analysis shows that the positive correlation results in this experiment cannot be used as an indicator of successful trajectory reconstruction based on a neural correlate. Several directions are herein discussed to address the misinterpretation of results as well as the implications on previous invasive and non-invasive works

Using supervised learning algorithms as a follow-up method in the search of gravitational waves from core-collapse supernovae

We present a follow-up method based on supervised machine learning (ML) to improve the performance in the search of gravitational wave (GW) bursts from core-collapse supernovae (CCSNe) using the coherent WaveBurst (cWB) pipeline. The ML model discriminates noise from signal events by using a set of reconstruction parameters provided by cWB as features. Detected noise events are discarded yielding a reduction in the false alarm rate (FAR) and the false alarm probability thus enhancing the statistical significance. We tested the proposed method using strain data from the first half of the third observing run of advanced LIGO, and CCSNe GW signals extracted from 3D simulations. The ML model is tuned using a dataset of noise and signal events, and then used to identify and discard noise events in the cWB analyses. Noise and signal reduction levels were examined in single (L1 and H1) and two detector network (L1H1). The FAR was reduced by a factor of ∼10 to ∼100 resulting in an enhancement in the statistical significance of ∼1σ to ∼2σ, while not impacting the detection efficiencies

Using Supervised Learning Algorithms as a Follow-Up Method in the Search of Gravitational Waves from Core-Collapse Supernovae

We present a follow-up method based on supervised machine learning (ML) to improve the performance in the search of gravitational wave (GW) bursts from core-collapse supernovae (CCSNe) using the coherent WaveBurst (cWB) pipeline. The ML model discriminates noise from signal events by using a set of reconstruction parameters provided by cWB as features. Detected noise events are discarded yielding a reduction in the false alarm rate (FAR) and the false alarm probability thus enhancing the statistical significance. We tested the proposed method using strain data from the first half of the third observing run of advanced LIGO, and CCSNe GW signals extracted from 3D simulations. The ML model is tuned using a dataset of noise and signal events, and then used to identify and discard noise events in the cWB analyses. Noise and signal reduction levels were examined in single (L1 and H1) and two detector networks (L1H1). The FAR was reduced by a factor of ∼10 to ∼100 resulting in an enhancement in the statistical significance of ∼1σ to ∼2σ, while not impacting the detection efficiencies

Detecting and reconstructing gravitational waves from the next galactic core-collapse supernova in the advanced detector era

We performed a detailed analysis of the detectability of a wide range of gravitational waves derived from core-collapse supernova simulations using gravitational-wave detector noise scaled to the sensitivity of the upcoming fourth and fifth observing runs of the Advanced LIGO, Advanced Virgo, and KAGRA. We use the coherent WaveBurst algorithm, which was used in the previous observing runs to search for gravitational waves from core-collapse supernovae. As coherent WaveBurst makes minimal assumptions on the morphology of a gravitational-wave signal, it can play an important role in the first detection of gravitational waves from an event in the Milky Way. We predict that signals from neutrino-driven explosions could be detected up to an average distance of 10 kpc, and distances of over 100 kpc can be reached for explosions of rapidly-rotating progenitor stars. An estimated minimum signal-to-noise ratio of 10–25 is needed for the signals to be detected. We quantify the accuracy of the waveforms reconstructed with coherent WaveBurst and we determine that the most challenging signals to reconstruct are those produced in long-duration neutrino-driven explosions, and models that form black holes a few seconds after the core bounce

An Optically Targeted Search for Gravitational Waves emitted by Core-Collapse Supernovae during the Third Observing Run of Advanced LIGO and Advanced Virgo

We present the results from a search for gravitational-wave transients associated with core-collapse supernovae observed optically within 30 Mpc during the third observing run of Advanced LIGO and Advanced Virgo. No gravitational wave associated with a core-collapse supernova has been identified. We then report the detection efficiency for a variety of possible gravitational-wave emissions. For neutrino-driven explosions, the distance at which we reach 50% detection efficiency is up to 8.9 kpc, while more energetic magnetorotationally-driven explosions are detectable at larger distances. The distance reaches for selected models of the black hole formation, and quantum chromodynamics phase transition are also provided. We then constrain the core-collapse supernova engine across a wide frequency range from 50 Hz to 2 kHz. The upper limits on gravitational-wave energy and luminosity emission are at low frequencies down to 10−4M⊙c2 and 5×10−4M⊙c2/s, respectively. The upper limits on the proto-neutron star ellipticity are down to 5 at high frequencies. Finally, by combining the results obtained with the data from the first and second observing runs of LIGO and Virgo, we improve the constraints of the parameter spaces of the extreme emission models. Specifically, the proto-neutron star ellipticities for the long-lasting bar mode model are down to 1 for long emission (1 s) at high frequency

Caracterización de imaginación motora utilizando análisis de descomposición de bandas de energía

This article provides an overview of the analysis of electrical signals generated by the brain, when a person imagines themovement of their upper extremities. The signals studied are acquired with a brain-computer interface equipment and analyzed using DiscreteWavelet transform as the energy band decomposition technique. The use of this technique is new in the analysis of electrical signals generated bythe brain and the results obtained indicate that the use of the technique is feasible for the characterization of electrical signals coming from thebrain.Este artículo trata sobre el análisis de las señales eléctricas generadas por el cerebro, cuando una persona imagina elmovimiento de sus extremidades superiores. Las señales que se estudian son adquiridas con un equipo de interfaz cerebro-computador y sonanalizadas utilizando la transformada Wavelet Discreta para la técnica de descomposición de banda de energía. El uso de esta técnica es novedosoen el análisis de las señales eléctricas generadas por el cerebro y los resultados que se obtuvieron indican que el uso de la técnica es factible parala caracterización de señales eléctricas provenientes del cerebro

Population properties of compact objects from the second LIGO-Virgo gravitational-wave transient catalog

We report on the population of 47 compact binary mergers detected with a false-alarm rate of (BBH) population not discernible until now. First, the primary mass spectrum contains structure beyond a power law with a sharp high-mass cutoff; it is more consistent with a broken power law with a break at 39.7-+9.120.3 M? or a power law with a Gaussian feature peaking at 33.1-+5.64.0 M? (90% credible interval). While the primary mass distribution must extend to ~65 M? or beyond, only 2.9-+1.73.5% of systems have primary masses greater than 45 M?. Second, we find that a fraction of BBH systems have component spins misaligned with the orbital angular momentum, giving rise to precession of the orbital plane. Moreover,12%-44% of BBH systems have spins tilted by more than 90°, giving rise to a negative effective inspiral spin parameter, ceff. Under the assumption that such systems can only be formed by dynamical interactions, we infer that between 25% and 93% of BBHs with nonvanishing ceff| \u3e 0.01 are dynamically assembled. Third, we estimate merger rates, finding RBBH = 23.9-+8.614.3 Gpc-3 yr-1 for BBHs and RBNS = 320-+240490 Gpc-3 yr-1 for binary neutron stars. We find that the BBH rate likely increases with redshift (85% credibility) but not faster than the star formation rate (86% credibility). Additionally, we examine recent exceptional events in the context of our population models, finding that the asymmetric masses of GW190412 and the high component masses of GW190521 are consistent with our models, but the low secondary mass of GW190814 makes it an outlier

Modeling the ongoing dynamics of short and long-range temporal correlations in broadband EEG during movement

Electroencephalogram (EEG) undergoes complex temporal and spectral changes during voluntary movement intention. Characterization of such changes has focused mostly on narrowband spectral processes such as Event-Related Desynchronization (ERD) in the sensorimotor rhythms because EEG is mostly considered as emerging from oscillations of the neuronal populations. However, the changes in the temporal dynamics, especially in the broadband arrhythmic EEG have not been investigated for movement intention detection. The Long-Range Temporal Correlations (LRTC) are ubiquitously present in several neuronal processes, typically requiring longer timescales to detect. In this paper, we study the ongoing changes in the dynamics of long- as well as short-range temporal dependencies in the single trial broadband EEG during movement intention. We obtained LRTC in 2 s windows of broadband EEG and modeled it using the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model which allowed simultaneous modeling of short- and long-range temporal correlations. There were significant (p < 0.05) changes in both broadband long- and short-range temporal correlations during movement intention and execution. We discovered that the broadband LRTC and narrowband ERD are complementary processes providing distinct information about movement because eliminating LRTC from the signal did not affect the ERD and conversely, eliminating ERD from the signal did not affect LRTC. Exploring the possibility of applications in Brain Computer Interfaces (BCI), we used hybrid features with combinations of LRTC, ARFIMA, and ERD to detect movement intention. A significantly higher (p < 0.05) classification accuracy of 88.3 ± 4.2% was obtained using the combination of ARFIMA and ERD features together, which also predicted the earliest movement at 1 s before its onset. The ongoing changes in the long- and short-range temporal correlations in broadband EEG contribute to effectively capturing the motor command generation and can be used to detect movement successfully. These temporal dependencies provide different and additional information about the movement

Gravitational-wave Constraints on the Equatorial Ellipticity of Millisecond Pulsars

© 2020. The American Astronomical Society. All rights reserved.. We present a search for continuous gravitational waves from five radio pulsars, comprising three recycled pulsars (PSR J0437-4715, PSR J0711-6830, and PSR J0737-3039A) and two young pulsars: the Crab pulsar (J0534+2200) and the Vela pulsar (J0835-4510). We use data from the third observing run of Advanced LIGO and Virgo combined with data from their first and second observing runs. For the first time, we are able to match (for PSR J0437-4715) or surpass (for PSR J0711-6830) the indirect limits on gravitational-wave emission from recycled pulsars inferred from their observed spin-downs, and constrain their equatorial ellipticities to be less than 10-8. For each of the five pulsars, we perform targeted searches that assume a tight coupling between the gravitational-wave and electromagnetic signal phase evolution. We also present constraints on PSR J0711-6830, the Crab pulsar, and the Vela pulsar from a search that relaxes this assumption, allowing the gravitational-wave signal to vary from the electromagnetic expectation within a narrow band of frequencies and frequency derivatives

Diving below the Spin-down Limit: Constraints on Gravitational Waves from the Energetic Young Pulsar PSR J0537-6910

We present a search for quasi-monochromatic gravitational-wave signals from the young, energetic X-ray pulsar PSR J0537-6910 using data from the second and third observing runs of LIGO and Virgo. The search is enabled by a contemporaneous timing ephemeris obtained using Neutron star Interior Composition Explorer (NICER) data. The NICER ephemeris has also been extended through 2020 October and includes three new glitches. PSR J0537-6910 has the largest spin-down luminosity of any pulsar and exhibits fRequent and strong glitches. Analyses of its long-term and interglitch braking indices provide intriguing evidence that its spin-down energy budget may include gravitational-wave emission from a time-varying mass quadrupole moment. Its 62 Hz rotation frequency also puts its possible gravitational-wave emission in the most sensitive band of the LIGO/Virgo detectors. Motivated by these considerations, we search for gravitational-wave emission at both once and twice the rotation frequency from PSR J0537-6910. We find no signal, however, and report upper limits. Assuming a rigidly rotating triaxial star, our constraints reach below the gravitational-wave spin-down limit for this star for the first time by more than a factor of 2 and limit gravitational waves from the l = m = 2 mode to account for less than 14% of the spin-down energy budget. The fiducial equatorial ellipticity is constrained to less than about 3 ×10-5, which is the third best constraint for any young pulsar