EEG and Brain-Computer Interfaces

Across-speaker articulatory reconstruction from sensorimotor cortex for generalizable brain-computer interfaces

This paper reconstructs articulatory features from brain signals using generalizable features from able-bodied individuals, achieving high correlation in three participants.

Automatic sleep scoring for real-time monitoring and stimulation in individuals with and without sleep apnea

This study evaluates automatic sleep scoring using EEG data from healthy and sleep apnea patients, finding that single-channel scoring is reliable and real-time methods match offline accuracy, though performance drops in clinical populations.

Towards Translational Sleep Staging: A Cross-Species Deep-Learning Model for Rodent and Human EEG

This paper develops a cross-species deep learning model for sleep staging in humans and rodents, achieving high accuracy without retraining.

Closed-loop error damping in human BCI using pre-error motor cortex activity

This paper introduces error modulation in motor BCIs using pre-error motor cortex activity to improve cursor control performance during online BCI tasks.

A Wearable Brain--Computer Interface for Mitigating Car Sickness via Attention Shifting

This paper introduces a wearable BCI system using AI to mitigate car sickness through attention shifting, showing over 83% effectiveness in reducing symptoms.

Spatial filters - in search of the virtual electrode

This paper reviews spatial filters and their limitations in brain data analysis, focusing on virtual electrodes and their practical constraints.

Classification of the lower limb motor imagery and execution using high vs. low channel EEG devices

This paper compares the performance of high-channel and low-channel EEG devices in classifying lower limb motor imagery and execution tasks.

Automated Detection of Quiet and Non-Quiet Sleep in Preterm Neonates from aEEG: Towards Predicting Brain Maturation

This paper develops an automated method to detect quiet and non-quiet sleep in preterm neonates using aEEG, showing strong correlation with brain maturation.

Compositional decoding of neural activity enhances generalization in handwriting BCIs

This paper introduces a compositional decoding model for handwriting BCIs that improves generalization by leveraging behavioral motifs.

An intracortical brain-machine interface based on macaque ventral premotor activity

This study compares the effectiveness of using neural activity from ventral premotor cortex F5c, dorsal premotor cortex, and primary motor cortex for motor brain-machine interface control, finding comparable or better performance from F5c.

CoPrimeEEG: CRT-Guided Dual-Branch Reconstruction from Co-Prime Sub-Nyquist EEG

CoPrimeEEG reconstructs high-rate EEG from low-rate co-prime samples using a dual-branch neural network with a CRT-guided loss, achieving state-of-the-art quality with fewer parameters.

Preserved Embedding of Neural Population Activity for Brain Computer Interfaces

The paper introduces MANDT, a method that extracts consistent embeddings of motor cortical dynamics to improve BCI decoding across sessions and tasks, validated on rhesus monkey data.

Demonstration of periodic and aperiodic EEG reliability between laboratory and clinic settings

This study compares EEG data quality and reliability between laboratory and clinic settings, finding high agreement in periodic and aperiodic features across age groups.

Enhancing fMRI decoded neurofeedback with co-adaptive training: simulation and proof-of-principle evidence

This paper introduces a co-adaptive decoding method for fMRI neurofeedback that improves training performance through real-time decoder updates, supported by simulations and real data.

ExSEnt for explainable dementia detection: disentangling temporal and amplitude-driven complexity boosts EEG-based classification

This paper introduces ExSEnt, a novel entropy-based measure for dementia detection using EEG, showing it achieves comparable classification performance to deep learning methods with a simpler logistic regression model.

Symmetric Fusion of fMRI and EEG for Spectrally Resolved Functional Neuroimaging

FSINC improves EEG-fMRI source imaging by integrating frequency-resolved EEG with fMRI via a data-driven neurovascular coupling model, achieving better spatial and temporal accuracy in both simulated and real data.

Uncertainty in Deep Learning for EEG under Dataset Shifts

This study evaluates how ensemble learning strategies affect performance and uncertainty estimation in EEG classification under dataset shifts.

G3DCT: An Interpretable Spatial Grid-based Framework with Temporal Convolution-Transformer for EEG Artifact Identification

G3DCT is a deep learning framework for EEG artifact identification that outperforms existing models, achieving a 2.8% F1-score improvement on combined artifacts.

Toward navigating emotional states using real-time representational similarity analysis fMRI neurofeedback - a feasibility study

This study explores using real-time representational similarity analysis in fMRI neurofeedback to navigate emotional states, showing feasibility and improved performance with repeated sessions.

Fail closed trust gated synthetic augmentation governs tail risk under subject shift in EEG

The paper introduces trust-gated augmentation to reduce harm from synthetic data in EEG tasks under subject shift, showing improved generalization and reduced performance degradation.

Task-Dependent Effects of Channel Density on Multivariate Pattern Analysis and Spatial Representational Similarity Analysis: Evidence from Three EEG Datasets

The paper evaluates how electrode density affects EEG decoding and RSA across three cognitive tasks, finding task-dependent impacts and suggesting low-density setups can be sufficient.

Predicting driver distraction using a single channel ear EEG

This study decodes cognitive distraction during driving using single-channel in-ear EEG, showing comparable temporal dynamics to full-cap scalp EEG and highlighting eye velocity as an early behavioral marker.

The potential of ensemble-based automated sleep staging on single-channel EEG signal from a wearable device

This paper tests an ensemble of sleep staging algorithms on single-channel EEG from a wearable device, achieving high accuracy comparable to expert PSG scoring.

Time-series models can predict long periods of human temporal EEG responses to randomly alternating visual stimuli

The paper uses time-series models to predict human EEG responses to visual stimuli and finds the simplest AR model performs as well as more complex models.

Cross-brain transfer of high-performance intracortical speech and handwriting BCIs

This paper demonstrates cross-brain transfer learning for intracortical BCIs decoding speech and handwriting, showing improved performance with limited training data and specific decoder design.

Towards Multi-Brain Decoding in Autism: A Self-Supervised Learning Approach

This study uses self-supervised learning to classify autism spectrum condition from hyperscanning EEG data during social interactions, achieving 78.13% accuracy.

EEG-based Decoding of Auditory Attention to Conversations with Turn-taking Speakers

This paper evaluates auditory attention decoding in conversation-tracking scenarios, finding that accuracy is not affected by attention direction or switches, and correlates with perceived listening effort.

A portable cortical evoked potential operant conditioning system (C-EPOCS): System development

This paper develops a portable C-EPOCS system for real-time neurofeedback, showing that lower-resolution EMG signals can still reliably estimate key parameters for stimulation intensity.

Robust Neural Decoding with low density EEG

This study investigates how electrode density affects EEG-based neural decoding accuracy using visual and natural stimuli, finding that reliable decoding is possible even with low-density setups.

Dynamic Graphs Analysis of EEG

This study uses dynamic graph neural networks to classify EEG signals and analyze temporal changes in brain connectivity for epilepsy and healthy subject differentiation.