EEG and Brain-Computer Interfaces

A Multi-subject, Multi-session, and Multi-task database for EEG-based Biometrics Challenge

The paper introduces a large-scale EEG database and competition to study EEG commonality and variability across subjects, sessions, and tasks, aiming to improve machine learning algorithms for EEG-based biometrics.

Scale-free and oscillatory spectral measures of sleep stages in humans

The paper analyzes sleep EEG spectra using the FOOOF method to separate aperiodic and oscillatory components, finding sleep stage-specific spectral features.

The BeMoBIL Pipeline for automated analyses of multimodal mobile brain and body imaging data

Open-source MATLAB pipeline (EEGLAB, fieldtrip) that synchronizes mobile EEG with motion and eye-tracking data, extracts gait, blink and heartbeat events, and clusters independent components per region of interest.

Three simple steps to improve the interpretability of EEG-SVM studies

This paper identifies three critical steps to improve the interpretability and clinical adoption of EEG-SVM studies by addressing issues in technical reporting.

Simple Neurofeedback via Machine Learning: Challenges in real time multivariate assessment of meditation state

This paper develops a lightweight autoencoder model for real-time meditation state assessment using EEG data, showing that multivariate EEG features have substantial overlap and temporal dynamics not captured by simple one-class algorithms.

Assessing HD-EEG functional connectivity states using ahuman brain computational model

The paper evaluates EEG/MEG source-space network analysis techniques using a human brain computational model and identifies optimal methods for inverse modeling, connectivity measures, and dimensionality reduction.

EEG-neurofeedback for promoting neuromodulation in the elderly: evidence from a double-blind study

This study tests if EEG-neurofeedback can help elderly people modulate brain activity, finding significant self-modulation in trained subjects but no cognitive improvement.

Automated real-time EEG sleep spindle detection for brain state-dependent brain stimulation

This paper introduces a real-time EEG sleep spindle detection algorithm that enables automated stimulation during sleep spindles with high sensitivity and precision.

Improving classification and reconstruction of imagined images from EEG signals

This study improves EEG-based classification of imagined images and image reconstruction using a modified neural network with an attention module and GAN.

Electroencephalogram Data Collection for Student Engagement Analysis with Audio-Visual Content

This paper collects and provides EEG data from students exposed to audio-visual content to analyze engagement and attention during learning.

Preserved motor representations after paralysis

The paper shows that motor representations remain stable after paralysis, enabling brain-computer interfaces to restore motor functions using preserved neural patterns.

DEED: A Dataset for Dream-related Emotion Research

The paper introduces DEED, a dataset of EEG recordings from sleep to assess dream emotions, and validates it using machine learning.

No need for extensive artifact rejection for ICA - A multi-study evaluation on stationary and mobile EEG datasets

The paper evaluates the impact of movement intensity and automatic sample rejection on ICA decomposition quality across multiple EEG datasets, finding that moderate cleaning improves decomposition regardless of movement intensity.

Meggie - easy-to-use graphical user interface for M/EEG analysis based on MNE-python

Meggie provides an open-source, Python-based GUI for M/EEG analysis, enabling end-to-end processing with multi-subject support and plugin extendibility.

Bayesian models for event-related potentials time-series and electrode correlations estimation

The paper introduces Bayesian Gaussian random walk models to estimate ERP voltage over time and electrode correlations using real P3b data.

Learning neural decoders without labels using multiple data streams

This paper develops cross-modal, self-supervised neural decoders using multiple data streams to achieve high decoding accuracy without labeled data.

A reusable benchmark of brain-age prediction from M/EEG resting-state signals

This paper benchmarks machine learning approaches for brain age prediction using M/EEG signals across four international cohorts, finding that spatially aware models achieve the highest performance.

A New Paradigm for Investigating Real-World Social Behavior and its Neural Underpinnings

The paper describes an immersive paradigm for studying natural social behavior and its neural correlates in epilepsy patients using synchronized behavioral and neurophysiological data.

The relationship between frequency content and representational dynamics in the decoding of neurophysiological data

This paper identifies a frequency doubling artifact in decoding accuracy metrics from neurophysiological data and proposes a filtering approach and alternative decoding method to mitigate it.

Multi-Domain Characterization and Rapid Detection Technology for Cocaine and Methamphetamine Acute Abuse based on EEG

The paper proposes a multi-domain EEG analysis method using ResNet50 to detect acute cocaine and methamphetamine abuse in mice, achieving an F1-score of 95.10%.

The HAPPE plus Event-Related (HAPPE+ER) Software: A Standardized Processing Pipeline for Event-Related Potential Analyses

The paper introduces HAPPE+ER, an automated ERP processing pipeline for EEG data, validated with adult and developmental data to improve standardization and efficiency.

A transformer model for predicting cognitive impairment from sleep

This paper presents a transformer model to predict cognitive impairment from sleep EEG data, achieving 70.22% accuracy, outperforming traditional methods.

Imaging cortical engagement during motor imagery, mental arithmetic, and silent word generation using MEG beta rhythm

This study maps cortical engagement during motor imagery and cognitive tasks using MEG beta rhythm changes, showing task-specific patterns and high classification accuracy.

A Model Visualization-based Approach for Insight into Waveforms and Spectra Learned by CNNs

This paper introduces a model visualization approach to analyze CNN filters for EEG waveforms and spectral features, demonstrating its use in sleep stage classification.

PELP: accounting for missing data in neural time series by Periodic Estimation of Lost Packets

PELP estimates missing data in neural time series from implanted devices by determining the period of stimulation and accounting for data loss during active stimulation.

Executed and imagined grasping movements can be decoded from lower dimensional representation of distributed non-motor brain areas.

This paper decodes executed and imagined grasping movements from non-motor brain areas using a lower dimensional representation, achieving high accuracy without primary motor cortex signals.

Real-time neurofeedback to alter interpretations of a naturalistic narrative

This study uses real-time fMRI neurofeedback to influence interpretations of a naturalistic narrative, finding that higher decoding accuracy correlates with successful interpretation shifts.

Automated methodology for optimal selection of the minimum electrode subset for accurate EEG source estimation based on Genetic Algorithm optimization

This paper optimizes EEG electrode selection using a genetic algorithm to maintain high localization accuracy with fewer electrodes.

Magnetoencephalography can reveal deep brain network activities linked to memory processes

This study uses MEG and blind source separation to identify mesial brain activity linked to memory tasks, validated with intracerebral EEG.

Machine Learning Electroencephalography Biomarkers Predictive of Epworth Sleepiness Scale

This study uses machine learning to identify EEG biomarkers predictive of Epworth Sleepiness Scale scores, achieving over 80% accuracy in distinguishing high vs low ESS groups.