EEG and Brain-Computer Interfaces

The Dry Truth: Hair-Wetting Improves Dry Electrode EEG Signal Quality

This study shows that wetting hair with tap water improves dry electrode EEG signal quality by reducing impedance and increasing spectral and functional connectivity similarity to gel-based systems.

The DYNAM-O Toolbox: Characterizing Individualized Neural Signatures in Sleep EEG

The paper introduces DYNAM-O, a toolbox for analyzing sleep EEG to identify individualized transient oscillations, finding sex differences in oscillatory activity.

Can the heartbeat-evoked potential (HEP) be separated from Cardiac Artefact (CA) using beamforming?

This paper validates EEG beamforming for separating heartbeat-evoked potentials (HEP) from cardiac artefact (CA) using simulated and empirical data, showing reliable recovery and group differences in interoceptive regions.

Conditional Spatial Classification of Expert-Confirmed Interictal Epileptiform Discharge Epochs: An EEG-ECG Ablation and SHAP Analysis

This paper classifies expert-confirmed interictal epileptiform discharge epochs using EEG and ECG data, finding that ECG features improve categorization accuracy.

Quantum machine learning for detection of sleep deprivation from EEG signals

This study uses quantum machine learning models to detect sleep deprivation from EEG signals, achieving high accuracy in both epoch-level and subject-level evaluations.

Polar Geometry of Time-Odd EEG Dynamics

The paper introduces a polar decomposition method to analyze time-odd EEG dynamics across sessions, improving cross-session subject discrimination.

Img2EEG: A Scalable and Interpretable Encoding Framework for Simulating Human EEG Responses to Visual Inputs

Img2EEG generates synthetic EEG responses to visual inputs, generalizing across stimuli and participants while preserving neural structure and enabling targeted interventions.

MERIT: A No-Code Platform for Unifying High-Precision Cognitive Assessment Across Laboratory and Real-World Settings

The paper evaluates timing error tolerance in cognitive and neural measures across devices and introduces MERIT, a no-code platform for consistent cognitive assessment in lab and real-world settings.

An Interpretable EEG Machine Learning Prototype for Alzheimers Disease Classification

This paper develops an interpretable machine learning classifier for Alzheimer's disease using resting-state EEG data from OpenNeuro, achieving high accuracy with EEG Logistic Regression.

Neural encoding of grasp and object properties in the posterior parietal and motor cortices of the cortical grasping network in tetraplegic humans

This study investigates how grasp and object properties are encoded in the cortical grasping network of tetraplegic humans, showing separable neural representations in different brain regions that could improve brain-machine interface performance.

Estimating mutual information and Pearson correlation on neural evoked responses

This paper compares Pearson correlation and mutual information estimators for analyzing neural evoked responses, identifying parameter settings and trade-offs for reliable similarity measures.

Pre-movement sensor-level EEG activity recorded with a portable three-channel system is associated with futsal free-kick outcomes

The paper uses a portable three-channel EEG system to decode futsal free-kick outcomes from pre-movement brain activity, finding session-specific neural signatures linked to performance.

FUSE: FUsing EEG-MEG in a Shared Embedding via self-supervised learning for BCI

This paper introduces a two-phase deep learning framework for EEG-MEG decoding in BCI, showing improved classification accuracy and transferability across participants.

Tracking propagating cortical activity in MEG/EEG with a bilinear state-space model

This paper introduces a bilinear state-space model to track cortical traveling waves in MEG/EEG data, showing improved accuracy over traditional methods in simulations and empirical recordings.

CORSICA: Reproducible Suppression of Cochlear Implant Artifacts in EEG evoked by Continuous Speech

CORSICA suppresses cochlear implant artifacts in speech-evoked EEG data using cross-correlation with speech stimuli, outperforming alternative methods with minimal manual intervention.

The Effect of Visual Impairment on the Controllability of a Brain-Computer Interface Using Visual Spatial Attention

This study examines how visual impairment affects brain-computer interface performance using visual spatial attention, finding that while ERPs change, BCI decoding accuracy remains unaffected.

CREST: A Cortical Resting-State EEG Spatial Transformer for Chronic Pain Inference

The paper uses resting-state EEG data and a spatial transformer model to classify chronic pain status with an AUROC of 0.782.

AVOCODO: An open-source multimodal annotation platform for developmental EEG

AVOCODO integrates behavioral annotation into EEG workflows, improving efficiency and reproducibility in developmental EEG research.

Genuinely Blind Identification of Sleep Spindles through Trispectral Modulation Analysis

This paper introduces a blind method to identify sleep spindles using trispectral modulation analysis, achieving high agreement with expert annotations and detecting more spindles than conventional methods.

CViT-ESP: Lightweight Pre-trained Vision Transformers for EEG-based Epileptic Seizure Prediction

This paper introduces CVIT-ESP and ESPFormer, pre-trained Vision Transformer models adapted for EEG-based seizure prediction, achieving higher AUC-ROC than existing models.

LAND: Latent Aligned Neural-Behavioral Dynamics via Flow Matching forGeneralizable Movement Decoding

The paper introduces LAND, a framework for improving movement decoding in BCIs by aligning neural and behavioral dynamics using unpaired data, achieving better generalization across movement speeds and subjects.

Reliability and disease sensitivity are dissociable properties of EEG foundation-model representations

The paper evaluates EEG foundation models for reliability and disease discrimination, finding they are partially dissociable properties with distinct contributing factors.

EEG Microstate Sequences as Potential Brain-Computer Interface Triggers Derived from Motor Imagery Classification

This study explores using EEG microstate sequences as BCI triggers for motor imagery classification, achieving 89% accuracy with transfer learning and requiring ~400s for calibration.

BCIJelly: An integrated ecosystem for brain-computer interface research

BCIJelly provides a unified ecosystem for BCI research, standardizing data, integrating decoders, and enabling efficient deployment on neuromorphic hardware.

From Scalp to Source: Precise Phase Retrieval of Intracerebral Epileptic Sources Based on Surface EEG

This paper validates the Gabor-Nelson method for reconstructing deep-brain oscillatory phase from scalp EEG without anatomical imaging, achieving comparable accuracy to MRI-informed methods.

A Comprehensive Benchmark of EEG-Based BCI Deep Learning Models for MCI and Dementia Classification

This paper benchmarks ten deep learning models for MCI and dementia classification using EEG data, finding that compact architectures can be competitive with larger models.

A single-channel EEG classification system for multiscale characterization of mouse vigilance state

This paper develops a single-channel EEG framework for cross-animal sleep staging in mice, achieving high accuracy and preserving sleep architecture over long-term recordings.

Meditation Styles Are Highly Discriminable from EEG at the Subject Level With Limited Generalization Across the Population: A Machine-Learning Study

This study uses EEG and machine learning to classify meditation styles and mind-wandering, showing high accuracy within individuals but poor generalization across subjects.

EEG-based classification models reveal differential neural processing of words and images

The paper uses EEG data and support vector machines to classify neural responses to words and images, finding higher accuracy for images than words.

Time-frequency embedding with contrastive pre-training allows sub-second seizure detection

This paper introduces a 3D CNN with a trainable CWT layer and BiCL pre-training for sub-second seizure detection, achieving >95% accuracy on EEG data.