EEG and Brain-Computer Interfaces

Misinterpreting electrophysiology in human cognitiveneuroscience

The paper challenges the direct interpretation of EEG components in cognitive neuroscience by showing they are indirectly linked to cognition through oculomotor actions, using 6 experiments with 204 participants.

Decoupling simultaneous motor imagination and execution via orthogonal ECoG neural representations

This study decodes simultaneous motor execution and imagination using ECoG signals by identifying orthogonal neural subspaces.

Improved Source Localization of Auditory Evoked Fields using Reciprocal BEM-FMM

This paper improves the localization of auditory evoked fields using reciprocal BEM-FMM, achieving higher accuracy and focality compared to standard BEM methods.

An adversarial approach to guide the selection of preprocessing pipelines for ERP studies

This paper introduces an adversarial method to evaluate and compare EEG preprocessing pipelines by using simulated signals as ground truth to assess noise removal effectiveness.

A retrospective public external benchmark of healthy-to-stroke lower-limb EEG transport identifies constraints from source construction, adaptation burden, and confound sensitivity

The paper benchmarks healthy-to-stroke lower-limb EEG transport using public datasets, finding that source construction and adaptation burden are more important than model type.

BrainYears: A functional EEG-based brain age clock enables intervention-ready measurements of brain aging

The paper develops an EEG-based brain age clock using machine learning to predict chronological age and assess brain aging, showing a decrease in brain age with intervention.

Generalizable Finger Movement Decoding from Intracranial Recordings Across Static and Dynamic Actions

This paper shows that BCIs can generalize across different finger movements by optimizing decoding pipelines, using high gamma features and linear models for better cross-task transfer.

Lack of Consensus for Manual Mouse Sleep Scoring Limits Implementation of Automatic Deep Learning Models

This study shows that automatic deep learning models for mouse sleep scoring struggle to generalize across labs due to signal variability and label noise, and proposes four robust models trained on diverse data.

Comparative Analysis of In-Ear and On-Head EEG for Sports Applications

This paper demonstrates that alpha-band EEG signals can be reliably detected from an in-ear electrode during physical activity, enabling fatigue monitoring in sports.

EEG-ChiMamba: Towards a Robust Mamba-Based Architecture for Dementia Detection from Resting State Electroencephalography

This paper introduces EEG-ChiMamba, a state space model for dementia detection from rs-EEG, achieving 57.65% accuracy on a large dataset.

EEG Foundation Model Improves Online Directional Motor Imagery Brain-computer Interface Control

This paper introduces an EEG foundation model that improves online directional motor imagery BCI control, achieving higher accuracy and faster task completion in human participants.

Event-Related Warping: A Toolbox for Temporal Alignment and Jitter Correction in Sequential Experimental Paradigms

This paper introduces Event-Related Warping (ERW), a method for temporal alignment and jitter correction in sequential experimental paradigms, improving response recovery and preserving inter-channel relationships.

EEG Bad-Channel Detection Using Multi-Feature Thresholding and Co-Occurrence of High-Amplitude Transients

The paper introduces a MATLAB module for detecting bad EEG channels using multi-feature thresholding and co-occurrence of high-amplitude transients, with a focus on interpretability and human-in-the-loop validation.

Short-Lived EEG Synchrony Patterns for Alzheimer's Disease Diagnosis

This paper introduces a framework using EEG synchrony patterns to diagnose Alzheimer's disease, finding that synchronization latency between specific channels and frequency bands correlates with clinical scores and achieves high diagnostic accuracy.

Forward-projected cortical eigenmodes provide an efficient sensor-space representation of resting-state EEG

This paper introduces a sensor-space EEG representation using cortical eigenmodes and a forward head model, showing improved efficiency and reliability compared to existing methods.

Structural Eigenmodes of the Brain to Improve the Source Localisation of EEG: Application to Epileptiform Activity

This paper uses structural eigenmodes of the brain to improve EEG source localization in epilepsy, finding geometric eigenmodes slightly outperform connectome eigenmodes.

Designing a Model to Detect Beta Burst in EEG Using Nonlinear Dynamic Features Based on Machine Learning

This paper develops a machine learning model to classify beta bursts in EEG using nonlinear dynamic features, achieving high accuracy in distinguishing burst durations and task conditions.

Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection

This paper develops an EEG-based framework using CNNs and mel-spectrograms to detect schizophrenia, identifying brain lobe-specific performance and explainability with XAI techniques.

Neural Correlates of Listening States, Cognitive Load, and Selective Attention in an Ecological Multi-Talker Scenario

This paper uses EEG to classify listening states and decode attended speech in multi-talker scenarios, achieving high accuracy for attentional states but not cognitive load.

Pre-trained Vision Transformers for Seizure Prediction: A Reproducible Baseline with Event-Based Evaluation and Statistical Validation

This paper evaluates pre-trained vision transformers for seizure prediction using a reproducible benchmark, achieving improved performance with lower false alarms and higher partial AUC.

Decoding real world visual scenes from the human gamma band with flicker evoked oscillations.

This paper decodes real-world visual scenes from human gamma band SSVEPs using flicker evoked oscillations, showing high accuracy with short data and highlighting the importance of 40 Hz.

Neurophysiological Effects of Sudarshan Kriya Revealed by Subject-Independent EEG Machine Learning

This study uses EEG and machine learning to show that Sudarshan Kriya Yoga induces measurable neural changes detectable across subjects.

invertmeeg: A Benchmark and Unified Python Library for EEGInverse Solvers

The paper introduces a benchmark and Python library for EEG inverse solvers, showing no single method dominates all scenarios and highlighting the performance of flexible subspace and hybrid methods.

Characterizing EEG Spectro-Temporal Variability Signatures in Alzheimer's and Parkinson's Disease

This study identifies disease-specific spectro-temporal signatures in Alzheimer's and Parkinson's disease using EEG and machine learning, revealing increased variability and distinct spectral markers.

NeuroNarrator: A Generalist EEG-to-Text Foundation Model for Clinical Interpretation via Spectro-Spatial Grounding and Temporal State-Space Reasoning

NeuroNarrator translates EEG segments into clinical narratives using spectro-spatial grounding and temporal state-space reasoning, enabling interpretable clinical interpretation.

Small-world scale-free brain networks from EEG with application to motor imagery decoding and brain fingerprinting

This paper introduces a framework for constructing EEG-based brain graphs with small-world and scale-free properties, improving motor imagery decoding and brain fingerprinting performance.

Machine-Learning-Based spike marking in signal and source space EEG from a patient with focal epilepsy

This paper uses artificial neural networks to classify interictal epileptiform discharges in EEG data from a patient with focal epilepsy, achieving high accuracy in signal and source space analyses.

Exploring sex-related Biases in Deep Learning Models for Motor Imagery Brain-Computer Interfaces

This study analyzes sex-related biases in deep learning models for motor imagery BCIs, finding that performance disparities are mainly due to EEG pattern discriminability, not the models themselves.

Design and validation of a novel portable electrode holder for motor-related EEG measurement

The paper designs and validates a portable EEG electrode holder for accurate motor-related EEG measurement in real-world settings.

Interpretable EEG Biomarkers for Neurological Disease Models in Mice Using Bag-of-Waves Classifiers

The paper uses bag-of-waves classifiers to predict epilepsy genotypes in mice from EEG data, achieving high accuracy and specificity.