TMS-evoked potential propagation reflects effective brain connectivity.

Cognition is achieved through communication between brain regions. Consequently, there is considerable interest in measuring effective connectivity. A promising effective connectivity metric is transcranial magnetic stimulation (TMS) evoked potentials (TEPs), an inflection in amplitude of the electroencephalogram recorded from one brain region as a result of TMS applied to another region.

Using data from cue presentations results in grossly overestimating semantic BCI performance.

Neuroimaging studies have reported the possibility of semantic neural decoding to identify specific semantic concepts from neural activity. This offers promise for brain-computer interfaces (BCIs) for communication. However, translating these findings into a BCI paradigm has proven challenging.

A Growing Bubble Speller Paradigm for Brain-Computer Interface Based on Event-related Potentials.

Objective: Event-related potentials (ERPs) reflect electropotential changes within specific cortical regions in response to specific events or stimuli during cognitive processes. The P300 speller is an important application of ERP-based brain-computer interfaces (BCIs), offering potential assistance to individuals with severe motor disabilities by decoding their electroencephalography (EEG) to communicate.

Methods: This study introduced a novel speller paradigm using a dynamically growing bubble (GB) visualization as the stimulus, departing from the conventional flash stimulus (TF).

Inter-participant transfer learning with attention based domain adversarial training for P300 detection.

A Brain-computer interface (BCI) system establishes a novel communication channel between the human brain and a computer. Most event related potential-based BCI applications make use of decoding models, which requires training. This training process is often time-consuming and inconvenient for new users.

An Evaluation of Hybrid Deep Learning Models for Classifying Multiple Lower Limb Actions.

Brain-computer Interfaces (BCIs) interpret electroencephalography (EEG) signals and translate them into control commands for operating external devices. The motor imagery (MI) paradigm is popular in this context. Recent research has demonstrated that deep learning models, such as convolutional neural network (CNN) and long short-term memory (LSTM), are successful in a wide range of classification applications.

Novel channel selection model based on graph convolutional network for motor imagery.

Multi-channel electroencephalography (EEG) is used to capture features associated with motor imagery (MI) based brain-computer interface (BCI) with a wide spatial coverage across the scalp. However, redundant EEG channels are not conducive to improving BCI performance. Therefore, removing irrelevant channels can help improve the classification performance of BCI systems.

Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems.

Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information.

Classification of Motor Imagery Based on Multi-Scale Feature Extraction and the Channel-Temporal Attention Module.

Motor imagery (MI) is a popular paradigm for controlling electroencephalogram (EEG) based Brain-Computer Interface (BCI) systems. Many methods have been developed to attempt to accurately classify MI-related EEG activity. Recently, the development of deep learning has begun to draw increasing attention in the BCI research community because it does not need to use sophisticated signal preprocessing and can automatically extract features.

Management of a complex acetabular fracture following defibrillation for ventricular fibrillation cardiac arrest.

In this case report, we describe the first case of a patient who sustained a complex acetabular fracture following defibrillation for ventricular fibrillation cardiac arrest in the context of acute myocardial infarction. The patient was unable to undergo definitive open reduction internal fixation surgery due to the need to continue dual antiplatelet therapy following coronary stenting of his occluded left anterior descending artery. Following multidisciplinary discussions, a staged approach was opted for, with percutaneous closed reduction screw fixation of the fracture performed while the patient was maintained on dual antiplatelet therapy.

Neural decoding of music from the EEG.

Neural decoding models can be used to decode neural representations of visual, acoustic, or semantic information. Recent studies have demonstrated neural decoders that are able to decode accoustic information from a variety of neural signal types including electrocortiography (ECoG) and the electroencephalogram (EEG). In this study we explore how functional magnetic resonance imaging (fMRI) can be combined with EEG to develop an accoustic decoder.

Sonic enhancement of virtual exhibits.

Museums have widely embraced virtual exhibits. However, relatively little attention is paid to how sound may create a more engaging experience for audiences. To begin addressing this lacuna, we conducted an online experiment to explore how sound influences the interest level, emotional response, and engagement of individuals who view objects within a virtual exhibit.

Bispectrum-based hybrid neural network for motor imagery classification.

Background: The performance of motor imagery electroencephalogram (MI-EEG) decoding systems is easily affected by noise. As a higher-order spectra (HOS), the bispectrum is capable of suppressing Gaussian noise and increasing the signal-to-noise ratio of signals. However, the sum of logarithmic amplitudes (SLA) and the first order spectral moment (FOSM) features extracted from the bispectrum only use the numerical values of the bispectrum, ignoring the related information between different frequency bins.

Neural decoding of semantic concepts: a systematic literature review.

Semantic concepts are coherent entities within our minds. They underpin our thought processes and are a part of the basis for our understanding of the world. Modern neuroscience research is increasingly exploring how individual semantic concepts are encoded within our brains and a number of studies are beginning to reveal key patterns of neural activity that underpin specific concepts.

SincNet-Based Hybrid Neural Network for Motor Imagery EEG Decoding.

It is difficult to identify optimal cut-off frequencies for filters used with the common spatial pattern (CSP) method in motor imagery (MI)-based brain-computer interfaces (BCIs). Most current studies choose filter cut-frequencies based on experience or intuition, resulting in sub-optimal use of MI-related spectral information in the electroencephalography (EEG). To improve information utilization, we propose a SincNet-based hybrid neural network (SHNN) for MI-based BCIs.

Feature Extraction Method Based on Filter Banks and Riemannian Tangent Space in Motor-Imagery BCI.

Optimal feature extraction for multi-category motor imagery brain-computer interfaces (MI-BCIs) is a research hotspot. The common spatial pattern (CSP) algorithm is one of the most widely used methods in MI-BCIs. However, its performance is adversely affected by variance in the operational frequency band and noise interference.

A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery Based Brain-Computer Interface.

The motor imagery (MI) based brain-computer interfaces (BCIs) have been proposed as a potential physical rehabilitation technology. However, the low classification accuracy achievable with MI tasks is still a challenge when building effective BCI systems. We propose a novel MI classification model based on measurement of functional connectivity between brain regions and graph theory.

Feature selection method based on Menger curvature and LDA theory for a P300 brain-computer interface.

Brain-computer interface (BCI) systems decode electroencephalogram signals to establish a channel for direct interaction between the human brain and the external world without the need for muscle or nerve control. The P300 speller, one of the most widely used BCI applications, presents a selection of characters to the user and performs character recognition by identifying P300 event-related potentials from the EEG. Such P300-based BCI systems can reach good levels of accuracy but are difficult to use in day-to-day life due to redundancy and noisy signal.

The impact of COVID-19 on major trauma (ISS>15) in London, across its four Level 1 centres.

Introduction: The COVID-19 pandemic has led to reconfiguration of healthcare resources to manage increased demand for acute hospital beds and intensive care places. Concerns were raised regarding continuing provision of critical care for non-COVID patients during the pandemic. The aim of this study was to assess the impact of the COVID-19 pandemic on patients admitted with major trauma (Injury Severity Score >15) across the four Level 1 trauma centres in London.

Theatre efficiency in COVID-19 pandemic conditions: The collaborative experience of four level 1 major trauma centres in the UK.

Aims: As the world continues to fight successive waves of COVID-19 variants, we have seen worldwide infections surpass 100 million. London, UK, has been severely affected throughout the pandemic, and the resulting impact on the NHS has been profound. The aim of this study is to evaluate the impact of COVID-19 on theatre productivity across London's four major trauma centres (MTCs), and to assess how the changes to normal protocols and working patterns impacted trauma theatre efficiency.

Draft Genome Sequence of a Diploid and Hybrid Strain, UCD423, Isolated from Compost in Ireland.

Candida sanyaensis is a CUG-Ser1 clade yeast that is associated with soil. Assembly of short-read and long-read data shows that C. sanyaensis has a diploid and hybrid genome, with approximately 97% identity between the haplotypes.