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.

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.

Removal of physiological artifacts from simultaneous EEG and fMRI recordings.

Objective: Simultaneous recording of the electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) allows a combination of eletrophysiological and haemodynamic information to be used to form a more complete picture of cerebral dynamics. However, EEG recorded within the MRI scanner is contaminated by both imaging artifacts and physiological artifacts. The majority of the techniques used to pre-process such EEG focus on removal of the imaging and balistocardiogram artifacts, with some success, but don't remove all other physiological artifacts.

Enhancing P300 based character recognition performance using a combination of ensemble classifiers and a fuzzy fusion method.

Background: P300-based brain-computer interfaces provide communication pathways without the need for muscle activity by recognizing electrical signals from the brain. The P300 speller is one of the most commonly used BCI applications, as it is very simple and reliable, and it is capable of reaching satisfactory communication performance. However, as with other BCIs, it remains a challenge to improve the P300 speller's performance to increase its practical usability.

Optimization of Model Training Based on Iterative Minimum Covariance Determinant In Motor-Imagery BCI.

The common spatial patterns (CSP) algorithm is one of the most frequently used and effective spatial filtering methods for extracting relevant features for use in motor imagery brain-computer interfaces (MI-BCIs). However, the inherent defect of the traditional CSP algorithm is that it is highly sensitive to potential outliers, which adversely affects its performance in practical applications. In this work, we propose a novel feature optimization and outlier detection method for the CSP algorithm.

How to build a fast and accurate code-modulated brain-computer interface.

In the last decade, the advent of code-modulated brain-computer interfaces (BCIs) has allowed the implementation of systems with high information transfer rates (ITRs) and increased the possible practicality of such interfaces. In this paper, we evaluate the effect of different numbers of targets in the stimulus display, modulation sequences generators, and signal processing algorithms on the accuracy and ITR of code-modulated BCIs.We use both real and simulated electroencephalographic (EEG) data, to evaluate these parameters and methods.

Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification.

The common spatial patterns (CSP) algorithm is the most popular spatial filtering method applied to extract electroencephalogram (EEG) features for motor imagery (MI) based brain-computer interface (BCI) systems. The effectiveness of the CSP algorithm depends on optimal selection of the frequency band and time window from the EEG. Many algorithms have been designed to optimize frequency band selection for CSP, while few algorithms seek to optimize the time window.