EEG and Brain-Computer Interfaces

Myelin-informed forward models for M/EEG source reconstruction

This paper introduces myelin-informed forward models for M/EEG source reconstruction, showing improved accuracy using myelin density estimates from MRI.

The impact of CSF-filled cavities on scalp EEG and its implications

This paper shows that CSF-filled cavities significantly affect scalp EEG amplitude and topography, making group comparisons difficult to interpret without accounting for this confounding factor.

Transcranial Focused Ultrasound to V5 Enhances Human Visual Motion Brain-Computer Interface by Modulating Feature-Based Attention

This paper shows that targeting V5 with transcranial focused ultrasound improves a visual motion BCI task by enhancing feature-based attention through increased theta activity and preserved dorsal pathway connections.

'Are you even listening?' - EEG-based decoding of absolute auditory attention to natural speech

This paper decodes absolute auditory attention using EEG features, finding that neural envelope tracking is more reliable than spectral entropy for distinguishing active listening from distractor conditions.

Brain-state modeling using electroencephalography: Application to adaptive closed-loop neuromodulation for epilepsy

This paper introduces BrainState, a self-supervised method to model complex brain states from EEG data, applied to epilepsy patients to capture pre-seizure states and neuromodulation effects.

Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis

This study improves EEG-based MDD diagnosis using transfer learning by adapting representations from a sleep dataset to a higher sampling rate, increasing model accuracy and robustness.

Evaluating a Novel High-Density EEG Sensor Net Structure for Improving Inclusivity in Infants with Curly or Tightly Coiled Hair

The paper tests two high-density EEG sensor net designs in infants with curly or tightly-coiled hair, finding that the tall design improves data quality and retention.

Estimating steady-state evoked potentials in the limit of short data duration and low stimulation frequency.

This study evaluates methods for estimating low-frequency steady-state evoked potentials (SSEP) using short data recordings and identifies optimal parameters for reliable detection.

Removing EOG Artifacts From EEG Recordings using Deep Learning

The paper develops a deep learning model to remove EOG artifacts from EEG data using eye-tracking data, showing that ICA has low specificity for EOG removal.

Continuous and discrete decoding of overt speech with electroencephalography

This paper decodes speech features from EEG signals using deep learning models, achieving above-chance performance for both discrete and continuous speech tasks.

Computational Framework to Predict and Shape Human-Machine Interactions in Closed-loop, Co-adaptive Neural Interfaces

The paper develops a computational framework using control and game theory to predict and optimize user-decoder interactions in co-adaptive neural interfaces through experimental validation.

Feasibility of decoding covert speech in ECoG with aTransformer trained on overt speech

This study uses a Transformer model trained on overt speech to decode covert speech from ECoG data, achieving similar performance to models trained directly on covert speech.

Accuracy of dipole source reconstruction in the 3-layerBEM model against the 5-layer BEM-FMM model

The paper compares dipole source reconstruction accuracy between 3-layer and 5-layer BEM-FMM models, finding that 3-layer models provide good localization but less accurate orientation, with AMR improving 5-layer accuracy.

Direct Prediction of the Complex-Valued Analytic Signal of EEG from Raw Multichannel Data

This paper introduces a deep neural network framework that directly predicts the complex-valued analytic signal of EEG from raw data, outperforming traditional methods and showing robustness to missing electrodes.

EEG denoising during transcutaneous auricular vagus nerve stimulation across simulated, phantom and human data

The paper evaluates denoising techniques for EEG data during transcutaneous auricular vagus nerve stimulation, finding that GED/SASS and SSP are most effective in reducing artifacts while preserving signal integrity.

Towards an Eye-Brain-Computer Interface: Combining Gaze with the Stimulus-Preceding Negativity for Target Selections in XR

This paper combines gaze tracking with the Stimulus-Preceding Negativity (SPN) to improve target selection in XR by confirming user intent implicitly.

PyHFO: Lightweight Deep Learning-poweredEnd-to-End High-Frequency Oscillations AnalysisApplication

PyHFO is a lightweight deep learning platform for efficient HFO detection in EEG data, achieving 50x speed improvements over standard methods.

Delineating neural contributions to electroencephalogram-based speech decoding

This study uses Explainable AI to analyze uhd-EEG signals for speech decoding, showing neural contributions are distinct from EMG artifacts and can be enhanced by denoising.

Modulation of Human Frontal Midline Theta by Neurofeedback: A Systematic Review and Quantitative Meta-Analysis

This paper conducts a systematic review and meta-analysis of neurofeedback studies on frontal midline theta modulation in healthy humans, finding a medium effect size with high heterogeneity.

Decoded EEG Neurofeedback-Guided Cognitive Reappraisal Training for Emotion Regulation

This study develops an EEG neurofeedback-guided cognitive reappraisal training protocol for emotion regulation, showing improved performance for low positive valence stimuli with real-time feedback.

Bayesian Functional Connectivity and Graph Convolutional Network for Working Memory Load Classification

The paper classifies working memory loads using Bayesian functional connectivity and graph convolutional networks, achieving 96% accuracy in intrasubject classification.

Accurate localization of cortical and subcortical sources of M/EEG signals by a convolutional neural network with a realistic head conductivity model: Validation with M/EEG simulation, evoked potentials, and invasive recordings

This paper introduces a convolutional neural network for accurate M/EEG source localization, validated on simulations and real data, showing improved accuracy for both cortical and subcortical regions.

Mobile EEG for Neurourbanism Research - What Could Possibly Go Wrong?A Critical Review with Guidelines

This paper reviews Neurourbanism studies using mobile EEG, identifies methodological flaws, and provides guidelines for better practice.

A flexible intracortical brain-computer interface for typing using finger movements

This paper develops a flexible intracortical BCI for typing using finger movements, achieving high accuracy and speed in two typing paradigms.

Transitioning from global to local computational strategies during brain-machine interface learning

This study shows that during BMI learning, animals transition from a global strategy involving all neurons to a local strategy involving only direct neurons as they become experts.

A theory of brain-computer interface learning via low-dimensional control

This paper develops a theory explaining BCI learning through low-dimensional control strategies, validated with experimental data.

What and where in electromagnetic brain imaging

This paper introduces a method to infer brain activity sources and their locations from electromagnetic signals using linear analysis and anatomical models.

BRAND: A platform for closed-loop experiments with deep network models

BRAND provides a platform for closed-loop experiments with deep networks, achieving low latency and supporting multiple programming languages.

A growth chart of brain function from infancy to adolescence based on electroencephalography

This paper develops a functional brain age (FBA) measure from EEG data in children, showing high accuracy in tracking neurodevelopmental maturation.

Connectome spectrum electromagnetic tomography: a method to reconstruct electrical brain source-networks at high-spatial resolution

This paper introduces CSET, a method that reconstructs electrical brain source-networks with high spatial resolution using diffusion MRI and EEG data.