EEG and Brain-Computer Interfaces

Mind Captioning: Evolving descriptive text of mental content from human brain activity

This paper develops a method to generate descriptive text of mental content from brain activity, showing it can capture structured visual semantics and generalize to recalled content.

Automated Seizure Detection in Animal EEG Signals

This study evaluates GRU and LSTM for seizure detection in animal EEG data from rats exposed to kainate or Soman, showing better performance than classical models.

Does wearable neurofeedback training improve memory aptitudes in healthy adults?--A systematic review and meta-analysis

This study reviews and analyzes neurofeedback training's impact on memory in healthy adults, finding small-to-moderate improvements with varying factors influencing effectiveness.

Power-to-power cross-frequency coupling as a novel approach for temporal lobe seizure detection and analysis

This paper introduces power-to-power cross-frequency coupling as a method to detect and analyze temporal lobe seizures using intracranial EEG data.

Using Information Geometry to Characterize Higher-Order Interactions in EEG

This paper applies information geometry methods to EEG data to detect third-order neural interactions using optimized binarization and surrogate data validation.

Towards decoding inner speech from EEG and MEG

This paper investigates decoding inner speech using EEG and MEG, finding that silent reading can be decoded with moderate accuracy while inner speech tasks perform at chance level.

PhysioMotion Artifact: A Task-Driven EEG Dataset with Channel-Level Motion Artifact Annotations

The paper introduces a task-driven EEG dataset with channel-level motion artifact annotations and demonstrates a hybrid CNN-Transformer model for artifact detection and classification.

EEG is better when cleaning effectively targets artifacts

The paper shows that targeted artifact cleaning in EEG improves signal preservation and reduces effect size inflation and source localization biases compared to traditional methods.

Transformer Networks Enable Robust Generalization of Source Localization for EEG Measurements

This paper introduces a transformer-based model for EEG source localization that outperforms traditional and machine learning approaches across various noise and data conditions.

The footprint of colour in EEG signal

This paper decodes colour from EEG signals during natural vision using a neural network and shows that colour information is robustly represented even without semantic guidance.

Return of the GEDAI: Unsupervised EEG Denoising based on Leadfield Filtering

GEDAI improves EEG denoising by removing artifacts without clean references, outperforming existing methods in simulations and enhancing neurobehavioral predictions.

Effects of speaker and listener sex on auditory attention decoding performance

This study examines how speaker and listener sex affect auditory attention decoding performance using EEG and EOG measurements in young adults.

Tracking attention using RIFT with a consumer-monitor setup

This study demonstrates that covert attention can be reliably measured using RIFT on a 360Hz consumer monitor, expanding accessibility for cognitive neuroscience research.

Qualitative and Quantitative Comparative Analysis of Common Normal Variants and Physiological Artifacts in MEG and EEG

This study compares MEG and EEG in capturing physiological patterns and artifacts, finding MEG provides more spatially focused signals while EEG is more susceptible to high-amplitude artifacts.

Automated Classification of Sleep-Wake States and Seizures in Mice

The paper develops an automated classifier to identify sleep-wake states and seizures in mice, achieving high accuracy across different datasets.

SONIC: A Benchmarking Paradigm for Brain-Computer Interfaces

This paper introduces SONIC, a benchmarking paradigm for BCIs, demonstrating an ITR of over 200 bps using an ovine model and the Connexus BCI.

Spatio Temporal Attentional EEGNet: An Enhanced Deep Learning Model for Cognitive Workload Detection

This paper introduces STA-EEGNet, a deep learning model for cognitive workload detection using EEG signals, showing improved accuracy and interpretable insights into neural dynamics.

A lightweight, physics-based, sensor-fusion filter for real-time EEG denoising and improved downstream AI classification

The paper introduces a lightweight physics-based sensor-fusion filter for real-time EEG denoising that improves downstream AI classification without requiring training.

Preparatory encoding of diverse features of intended movement in the human motor cortex

This study investigates how preparatory activity in the human motor cortex encodes movement features like direction, curvature, and distance, and demonstrates its use for predictive brain-computer interface control.

High-Definition MEG Source Estimation using the Reciprocal Boundary Element Fast Multipole Method

This paper introduces a fast MEG source estimation method using reciprocal relationships and BEM-FMM, achieving high-resolution gain matrices for up to 1 million dipoles.

Postprocessing-Enhanced Machine Learning for Reliable Real-Time Sleep Staging in Closed-Loop Neuromodulation

This paper introduces a post-processing framework to improve real-time sleep staging reliability in neuromodulation by combining smoothing techniques and control logic.

Taguchi Design of Experiment for Optimizing Saline Solution Composition to Increase Electrical Coupling in a Multi-Electrode External EEG Sensor

This study uses Taguchi Design of Experiment to optimize saline solution composition for improving electrical coupling in a multi-electrode EEG sensor, finding Tween-20 as the dominant factor.

Learning Residual-based Biomarkers of Cognitive Health via Self-Supervised Learning on EEG State Transitions

This paper introduces a self-supervised learning framework for EEG-based biomarker discovery of cognitive health, but finds poor predictive performance using plasma p-tau217 as a proxy.

Converting Spectral Evoked-to-Background Ratio intoTime-Domain Signal-to-Noise Ratio - Validation for HighFrequency Oscillations

The paper validates a method to convert spectral evoked-to-background ratio into time-domain signal-to-noise ratio for high-frequency oscillations.

A groove brain-music interface for enhancing individual experience of urge to move

This study develops a groove brain-music interface (G-BMI) using EEG to create personalized playlists that enhance the urge to move by incorporating real-time neurofeedback.

Design and Implementation of a Decision Making System for Controlling a Hand Exoskeleton Based on EEG/EMG Signals

The paper develops a hybrid BCI system using EEG and EMG signals to control a hand exoskeleton for rehabilitation, achieving reliable classification of flexion and resting states.

Lost in a Large EEG Multiverse? Comparing Sampling Approaches for Representative Pipeline Selection

The paper compares sampling methods for selecting representative pipelines in multiverse analyses of EEG data to assess their impact on result robustness.

EEG Phase Slips Reveal Detailed Brain Activity Patterns of Novice Vipassana Meditators During Decision Making Tasks

The paper uses EEG phase slip rates and related biomarkers to identify differences in brain activity between novice Vipassana meditators and non-meditators during decision-making tasks.

CARACAS, a novel automated tool for Cardiac Artifact Removal in Absence of CArdiac Signal

CARACAS is an ECG-free tool for identifying cardiac independent components in EEG data, achieving high sensitivity and specificity compared to existing methods.

Moving beyond the motor cortex: a brain-wide evaluation of target locations for intracranial speech neuroprostheses

This study evaluates brain-wide regions for speech neuroprostheses, finding that sulcal depth areas provide better decoding accuracy than the motor cortex.