EEG and Brain-Computer Interfaces

Optimizing EEG source reconstruction with concurrent fMRI-derived spatial priors

This study compares EEG source reconstruction algorithms with different fMRI-derived spatial priors to determine the best approach for accurate source localization.

A Systematic Approach for Explaining Time and Frequency Features Extracted by CNNs from Raw EEG Data

This study introduces a model visualization approach to explain CNNs' time and frequency features from raw EEG data, showing consistency with clinical sleep stage classification guidelines.

Real-time audio processing of real-life soundscapes for EEG analysis: ERPs based on natural sound onsets

The paper develops Android apps for real-time EEG and audio synchronization to study natural sound-evoked ERPs in real-life settings.

EEG Alpha Power and Pupil Diameter Reflect Endogenous Auditory Attention Switching and Listening Effort

This paper evaluates an EEG-based attention decoder to detect attention switching and measures listening effort using EEG and pupillometry, finding significant differences in alpha power and pupil diameter during attentional switches.

On the clinical benefit of on-scalp MEG: A modeling study of on-scalp MEG epileptic activity source estimation ability

This paper compares the accuracy of epileptic activity source estimation using on-scalp MEG (osMEG) with other modalities and finds osMEG provides better accuracy than conventional MEG and high-density EEG.

A New Perspective on Individual Reliability beyond Group Effects for Event-related Potentials: A Multisensory Investigation and Computational Modeling

This study evaluates ERP test-retest reliability in adolescents, finding that stronger group effects do not guarantee higher individual reliability and that latency jitter and trial variability influence this relationship.

Automated Pipeline for Infants Continuous EEG (APICE): a flexible pipeline for developmental studies

APICE is an automated EEG preprocessing pipeline for infants that adapts to different ages and experimental conditions, validated on two datasets.

EEG Spectral Feature Modulations Associated with Fatigue in Robot-Mediated Upper Limb Gross Motor and Fine Motor Interactions

This study examines EEG spectral changes linked to fatigue during robot-mediated gross and fine motor tasks, finding task-specific brain activity patterns associated with physical and mental fatigue.

Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes

The paper introduces a multimodal neuroimaging dataset containing EEG, MRI, and behavioral data for studying visual evoked potentials and brain connectivity.

Variability of EEG electrode positions and their underlying brain regions: visualising gel artifacts from a simultaneous EEG-fMRI dataset

The paper examines variability in EEG electrode positions across participants using a simultaneous EEG-fMRI dataset, finding significant differences in electrode placement and the brain regions they cover.

A CNN model with feature integration for MI EEG subject classification in BMI

The paper proposes a multi-scale CNN model for EEG-based motor imagery classification in BMI, achieving 93.74% accuracy on BCI competition IV2b dataset.

EEG-based Graph Neural Network Classification of Alzheimer's Disease: An Empirical Evaluation of Functional Connectivity Methods

This study evaluates graph neural networks for Alzheimer's disease classification using EEG-based functional connectivity measures, finding that GNNs outperform baselines but no single FC measure consistently dominates.

Holo-Hilbert Spectral-based Noise Removal Method for EEG High-Frequency Bands

This paper introduces a noise removal method for EEG gamma-band signals using Holo-Hilbert spectral analysis to reduce artifacts while preserving the original signal.

A hybrid boundary element-finite element method to solve the EEG forward problem

This paper introduces a hybrid boundary element-finite element method to solve the EEG forward problem, combining the strengths of both methods to handle isotropic and anisotropic tissues.

Artifacts in EEG-based BCI therapies: friend or foe?

This study shows that artifacts in EEG signals are more informative than brain activity for classification, highlighting the need for careful feature design in BCI applications.

Improvement in peripherical visual attentional performance in professional soccer players following a single neurofeedback training.

This study shows that a single Alpha EEG-neurofeedback training session improves peripheral visual attentional performance in professional soccer players, with benefits lasting up to one month.

Validating the wearable MUSE headset for EEG spectral analysis and Frontal Alpha Asymmetry

The paper validates a low-cost wearable EEG system for measuring EEG spectral features comparable to a high-density system, showing reliable alpha power and asymmetry measurements.

A Toolbox and Crowdsourcing Platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE)

The paper introduces ALICE, a toolbox and crowdsourcing platform for automatic labeling of EEG independent components using machine learning and expert input.

Capturing cognitive events embedded in the real-world using mobile EEG and Eye-Tracking

The paper assesses the feasibility of capturing P300 ERP responses in real-world settings using mobile EEG and eye-tracking during library searches.

MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

MVPAlab is a MATLAB toolbox for analyzing multidimensional EEG and MEG data using machine learning, offering preprocessing, decoding, and statistical analysis tools.

Photorealistic reconstruction of visual texture from EEG signals

This paper reconstructs photorealistic texture images from EEG signals using an MVAE-based approach, showing that texture perception can be decoded from low-resolution neural data.

Exploratory methods for high-performance EEG speech decoding

This paper explores methods to improve EEG speech decoding by combining transcranial magnetic stimulation with delay differential analysis.

Teaching a computer to assess hypnotic depth: A pilot study

The paper uses machine learning on EEG data from hypnosis sessions to predict deep hypnosis levels in real time.

Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG

This paper introduces brMEGA, an algorithm for automated detection and removal of cardiogenic artifacts in single-channel EEG, showing effective artifact removal and potential for heart rate feature extraction.

Explainable Sleep Stage Classification with Multimodal Electrophysiology Time-series

This study classifies sleep stages using multimodal electrophysiology data and introduces an ablation method to assess modality importance, finding EEG is most important for most stages and EOG for REM and nonREM.

Cortical control of virtual self-motion using task-specific subspaces

This study develops a brain-machine interface for virtual self-motion by decoding neural activity from monkeys performing non-reaching arm movements, showing that different movement classes require distinct decoding strategies.

P300 Parameters Over the Lifespan: Validating Target Ranges from an In-Clinic Platform

The paper validates age-stratified target ranges for P300 metrics using clinical data and compares them to research trends, showing consistency in age-related changes.

State-of-the-art EEG artifact removal evaluation

This paper benchmarks EEG artifact removal methods using a novel dataset and evaluates the effectiveness of Independent Component Analysis (ICA) in isolating artifacts.

Automatic classification of ICA components from infant EEG using MARA

The paper adapts an adult ICA component classifier for infant EEG data, showing improved performance in artifact removal and classification accuracy compared to both the original adult classifier and manual methods.

Kernel-based Nonlinear Manifold Learning for EEG Channel Selection with Application to Alzheimer's Disease

This paper introduces a kernel-based nonlinear manifold learning method for EEG channel selection to distinguish Alzheimer's disease patients from healthy controls.