DGSD: Dynamical Graph Self-Distillation for EEG-Based Auditory Spatial Attention Detection

Auditory Attention Detection (AAD) aims to detect target speaker from brain signals in a multi-speaker environment. Although EEG-based AAD methods have shown promising results in recent years, current approaches primarily rely on traditional convolutional neural network designed for processing Euclidean data like images. This makes it challenging to handle EEG signals, which possess non-Euclidean characteristics. In order to address this problem, this paper proposes a dynamical graph self-distillation (DGSD) approach for AAD, which does not require speech stimuli as input. Specifically, to effectively represent the non-Euclidean properties of EEG signals, dynamical graph convolutional networks are applied to represent the graph structure of EEG signals, which can also extract crucial features related to auditory spatial attention in EEG signals. In addition, to further improve AAD detection performance, self-distillation, consisting of feature distillation and hierarchical distillation strategies at each layer, is integrated. These strategies leverage features and classification results from the deepest network layers to guide the learning of shallow layers. Our experiments are conducted on two publicly available datasets, KUL and DTU. Under a 1-second time window, we achieve results of 90.0\% and 79.6\% accuracy on KUL and DTU, respectively. We compare our DGSD method with competitive baselines, and the experimental results indicate that the detection performance of our proposed DGSD method is not only superior to the best reproducible baseline but also significantly reduces the number of trainable parameters by approximately 100 times

Microbacterium spp. peritonitis in patients undergoing peritoneal dialysis: a single-center experience and literature review

IntroductionPeritoneal dialysis-related peritonitis (PDRP) caused by Microbacterium spp. is very rare, with only 9 cases reported to date. In this study, we report the treatment experiences of 7 patients at our peritoneal dialysis center.MethodsWe retrospectively collected clinical characteristics and antibiotic management of all 7 episodes of PDRP caused by Microbacterium spp. in 7 patients from at our center over 4 years, and reviewed the documented Microbacterium spp. PDRP in the literature.ResultsEmpiric antibiotic therapy was initiated as soon as possible, and consisted of intraperitoneal (IP) gentamicin in combination with vancomycin. After up to 5 days, gentamicin was changed to meropenem if the treatment was not effective. The intended course of antibiotic treatment was 21-day. Totally, 6 episodes were cured (85.7%), which was higher than reported.ConclusionThe 21-day antibiotic therapy program by combining vancomycin and meropenem may benefit the management of Microbacterium spp. PDRP

Clinical and radiological characteristics of pediatric COVID-19 before and after the Omicron outbreak: a multi-center study

IntroductionThe emergence of the Omicron variant has seen changes in the clinical and radiological presentations of COVID-19 in pediatric patients. We sought to compare these features between patients infected in the early phase of the pandemic and those during the Omicron outbreak.MethodsA retrospective study was conducted on 68 pediatric COVID-19 patients, of which 31 were infected with the original SARS-CoV-2 strain (original group) and 37 with the Omicron variant (Omicron group). Clinical symptoms and chest CT scans were examined to assess clinical characteristics, and the extent and severity of lung involvement.ResultsPediatric COVID-19 patients predominantly had normal or mild chest CT findings. The Omicron group demonstrated a significantly reduced CT severity score than the original group. Ground-glass opacities were the prevalent radiological findings in both sets. The Omicron group presented with fewer symptoms, had milder clinical manifestations, and recovered faster than the original group.DiscussionThe clinical and radiological characteristics of pediatric COVID-19 patients have evolved with the advent of the Omicron variant. For children displaying severe symptoms warranting CT examinations, it is crucial to weigh the implications of ionizing radiation and employ customized scanning protocols and protective measures. This research offers insights into the shifting disease spectrum, aiding in the effective diagnosis and treatment of pediatric COVID-19 patients

Dataset analysis on Cu9S5 material structure and its electrochemical behavior as anode for sodium-ion batteries

The data presented in this data article are related to the research article entitled “Facile Synthetic Strategy to Uniform Cu9S5 Embedded into Carbon: A Novel Anode for Sodium-Ion Batteries” (Jing et al., 2018) [1]. The related experiment details of pure Cu9S5 has been stated. The structure data of pure Cu9S5 and the electrochemical performance for sodium-ion batteries are described

A Fully Automated Trial Selection Method for Optimization of Motor Imagery Based Brain-Computer Interface - Fig 13

<p><b>(A) The self-testing accuracies of S1_a dataset in st-BCI.</b> The red-circle marked accuracies (R₄₃, R₄₆) were apparently lower. <b>(B) The nine-channel EEG signals of the 43rd trial. (C) The nine-channel EEG signals of the 46th trial. (D)(E) The accuracies of session-to-session transfer with S1_a as training dataset and S1_b and S1_c as testing datasets respectively.</b></p

Layout of EEG electrodes with standard international 10–20 system.

<p>Layout of EEG electrodes with standard international 10–20 system.</p

Two examples of raw EEG data with different types of artifacts.

<p>(A) Raw 9-channel EEG signals of one trial in S1_4 dataset during motor-imagery and eyes-closed time segments respectively. (B) Raw 9-channel EEG signals of one trial in S1_5 dataset with obvious non-physiological artifacts.</p

The schematic drawing of the second-round data selection algorithm.

<p>The schematic drawing of the second-round data selection algorithm.</p

Illustration of proposed algorithm frame for ICA-based BCI system.

<p>Illustration of proposed algorithm frame for ICA-based BCI system.</p