EEG and Brain-Computer Interfaces

Longitudinal persistence of EEG brain fingerprints: phase and complex coherency geometry across sessions and functional states

The paper evaluates geometric representations of EEG coherency for longitudinal individual identification across different functional states and timeframes.

When Tagging Frequency Matters to Attention: Effects on SSVEPs, ERPs, and Cognitive Processing

This study shows that tagging frequency affects neural responses and cognitive performance in attention tasks using SSVEPs and ERPs.

Intracortical BCI Performance is Robust to Changes in Attentional Load During Dual-Tasking

This study examines how attentional load affects intracortical brain-computer interface (iBCI) performance during dual-tasking, finding robust performance but individual variability.

How well does electrical source imaging reconstruct neural time series? Evidence from simultaneous scalp and intracranial EEG recordings

This study evaluates how well electrical source imaging reconstructs neural time series using simultaneous scalp and intracranial EEG recordings, finding limited temporal reconstruction and spatial leakage.

A Personalized Approach to Student Mental Health: Real-Time EEG Biofeedback for Stress and Focus Management

This paper explores a personalized EEG biofeedback approach for stress and focus management using subject-specific thresholds and a two-channel EEG setup.

Disentangling evoked and induced activity from single-trial neurophysiological time series

This paper introduces EOIS, a framework to separate evoked, ongoing, and induced neural activity from single-trial data, enabling component-resolved analysis and applications in biomarker refinement and neuromodulation.

Localising the Seizure Onset Zone from Routine Interictal and Ictal Scalp EEG Via a Factorised Spatiotemporal Ensemble Deep Learning Framework

This paper develops a deep learning framework to localise the seizure onset zone from routine scalp EEG, achieving high accuracy and generalisation across datasets.

Frequency-Resolved Connectivity Graphs for EEG-Based Classification of Alzheimer and Frontotemporal Dementia

This paper develops an EEG-based graph model using frequency-resolved connectivity features to classify Alzheimer's and frontotemporal dementia against healthy controls, achieving competitive accuracy.

NED-Net: an annotation-training loop for rodent EEG event detection

NED-Net is an open-source tool for training and adapting seizure detectors in rodent EEG data, improving detection accuracy through iterative annotation and retraining.

Beyond Mean Pooling: Statistical Meta-Aggregation for Subject-Level EEG Classification in Parkinson's Disease

This paper introduces a statistical meta-aggregation method for EEG classification in Parkinson's disease, improving subject-level classification performance by aggregating epoch-wise predictions more effectively.

Comparing re-referencing methods for human intracranial EEG

This study compares re-referencing methods for human intracranial EEG data and finds method choice affects artefact rejection and signal characteristics but not memory effect detectability.

Detecting movement preparation with HYDRE: a hybrid model of temporal and spectral brain signals

This study introduces HYDRE, a hybrid model combining temporal, spectral, and Riemannian features to decode motor preparation from brain signals, achieving above-chance decoding up to 1400ms before movement.

Modeling Paralinguistic Features for Speech Neuroprostheses: A Systematic Review

This paper reviews research on integrating paralinguistic features into speech neuroprostheses to enhance emotional and expressive communication.

Real-time noise cancellation for EEG monitoring and brain-computer interfaces

This paper introduces rtSOUND, a real-time EEG noise cancellation algorithm that improves signal-to-noise ratio for brain-computer interfaces and monitoring applications.

EEG Pathology Detection via Clinical Concept Bottleneck Models

This paper introduces concept bottleneck models for EEG pathology detection, showing comparable performance to black-box models but with interpretability trade-offs.

Coupled neuro-fluid dynamics in human bioelectromagnetism

This study challenges the assumption that EEG and MEG signals are solely generated by neural activity, showing that fluid motion in the brain can produce signals resembling M/EEG.

Selective Auditory Attention Decoding with a Two-Node Wireless EEG Sensor Network

This paper evaluates selective auditory attention decoding using a wireless EEG sensor network, achieving 77.17% accuracy with a two-node system.

Somnus, a tunable, open-source platform for automated EEG sleep scoring in physiological and disease mouse models

Somnus automates sleep staging in mice using EEG, EMG, and video data, enabling rapid adaptation to disease models with minimal manual correction.

Volitional control of a recurrent brain computer interface via covert reinforcement of endogenous reward networks

This study shows rats can control a brain-computer interface using covert reinforcement by stimulating their own reward networks, enabling precise neural signal modulation without external rewards.

Design and Test Novel EEG Electrode Attachments for Type 4 Afro-textured Hairstyles

This study designs and tests 3D-printed EEG electrode attachments for Type 4 Afro-textured hair, showing improved signal quality and reduced electrode failure compared to conventional caps.

Resolution-aware spectral parameterization reveals robust context-dependent aperiodic EEG dynamics during real-world table tennis

The paper analyzes EEG data from table tennis to identify context-dependent aperiodic spectral dynamics during different play types, finding distinct spectral patterns between human-opponent and ball-machine scenarios.

Simultaneous multi-area recordings in macaque frontoparietal cortex during a manual object-interaction task

This paper presents a dataset of neural and kinematic recordings from a macaque performing complex hand-object interactions, showing variation in decoding performance across algorithms and channel availability.

A Semi-Dry, Visually Comfortable c-VEP BCI with Short Calibration, Conservative Idle-State Behavior, and a VR Proof of Concept

This paper introduces a c-VEP brain-computer interface with short calibration, high accuracy, and visual comfort, validated in both standard and virtual reality settings.

A User-Friendly Ear-EEG-Based Brain-Computer Interface Using Text Sequence Stimulation

This paper introduces a text-sequence stimulation paradigm for ear-EEG BCIs, achieving higher information transfer rates and reduced BCI illiteracy compared to conventional luminance-modulated SSVEP methods.

Effect of Large Language Models on P300 Speller Performance with Cross-Subject Training

This study evaluates how large language models improve P300 speller performance, showing up to 75% gains in typing speed and approaching theoretical limits.

Involuntary facial muscle activity during imagined vocalisation contaminates EEG and enables emotion decoding

This paper decodes emotion from EEG during overt and imagined vocalisation, finding that involuntary facial muscle activity contaminates EEG and contributes to emotion decoding.

How Much Does Electrode Density Matter? A Multilevel Evaluation from EEG Signals to Cortical Networks

This study evaluates how electrode density impacts EEG analysis across multiple levels, finding that lower densities affect regional and source-level analyses more than global measures.

Assessing the Relative Impact of Grasp and Object on Inferior Frontal Gyrus Activity during a Grasping Task

This study investigates how grasp and object properties modulate inferior frontal gyrus activity during a grasping task, finding that grasp is the primary driver of neural modulation.

Resting state EEG microstates: is a new FORM all you need?

The paper introduces FORM, a method that captures microstate geometry from EEG data independently of microstate solutions, showing high explanatory power for microstate maps and template organization.

TEFAR: a lightweight, configurable framework for semi-automatic artefact component classification in EEG and TMS-EEG

TEFAR is a framework for semi-automatic artefact classification in EEG and TMS-EEG using FieldTrip, achieving high specificity and sensitivity in simulations.