EEG and Brain-Computer Interfaces

Enhancing Gesture Decoding Performance Using Signals from Posterior Parietal Cortex: A Stereo-Electroencephalograhy (SEEG) Study

This study combines SEEG signals from posterior parietal cortex (PPC) and primary sensorimotor cortex to improve gesture decoding accuracy in iEEG-based brain-machine interfaces.

Playing P300-based BCI VR game leads to changes in cognitive functions in healthy adults

This study shows that playing a P300-based BCI VR game improves cognitive functions like selective attention and mental inhibition in healthy adults.

The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging

This paper evaluates the Dreem headband as an alternative to polysomnography for EEG signal acquisition and sleep staging, showing strong correlation with gold-standard measurements and comparable sleep staging accuracy.

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

This paper develops predictive models for continuous biomedical outcomes from M/EEG data, comparing supervised spatial filtering and Riemannian geometry approaches, and finds Riemannian methods are more robust to anatomical distortions.

Detection of interictal epileptiform discharges: A comparison of on-scalp MEG and conventional MEG measurements

This paper benchmarks on-scalp MEG against conventional MEG in an epilepsy patient, showing on-scalp MEG detects more interictal epileptiform discharges.

Deep Learning provides exceptional accuracy to ECoG-based Functional Language Mapping for epilepsy surgery

This study develops a deep learning algorithm for ECoG-based functional language mapping, achieving 83.05% accuracy comparable to ESM, improving upon conventional ECoG-FM by 23%.

Detection of anomalous high frequency events in human intracranial EEG

The paper develops an algorithm to detect high-frequency events in intracranial EEG without relying on amplitude or morphology assumptions, finding it identifies more events in non-seizure zones and shows amplitude differences between seizure and non-seizure zones.

Simultaneous human intracerebral stimulation and HD-EEG: ground-truth for source localization methods

This paper provides an open dataset of EEG recordings from precisely known brain locations to evaluate source localization methods.

Applying stochastic spike train theory for high-accuracy MEG/EEG

This paper introduces a theory of stochastic spike timing and a method called SCA to accurately isolate neural sources in MEG/EEG data, outperforming PCA and ICA in separating overlapping brain signals.

Combining eye tracking with EEG: Effects of filter settings on EEG for trials containing task relevant eye-movements

This paper investigates how high-pass filter settings affect EEG data contaminated by task-relevant eye movements, showing that lower filters cause distortions.

Exploiting Electrophysiological Measures of Semantic Processing for Auditory Attention Decoding

The paper investigates whether combining semantic dissimilarity features with auditory envelope features improves decoding of auditory attention in multi-speaker contexts, finding a marginal improvement that may not justify the added complexity.

Estimates of cortical column orientation improveMEG source inversion

This study improves MEG source inversion by using white matter surface locations and correspondence-based orientation estimation, showing better performance in evoked response fitting.

The Maryland Analysis of Developmental EEG (MADE) Pipeline

The MADE pipeline automates EEG preprocessing for pediatric data, addressing artifact contamination and short recording periods, and provides quality reports.

Comparison of beamformer implementations for MEG source localization

This study compares LCMV beamformer implementations across four MEG toolboxes, finding reliable source localization at typical SNR but differences in sensitivity to preprocessing parameters and SNR effects.

How Sensitive are EEG Results to Preprocessing Methods: A Benchmarking Study

This study benchmarks EEG preprocessing methods by analyzing their impact on signal and event-related measures across multiple studies.

Real brains in virtual worlds: Validating a novel oddball paradigm in virtual reality

This paper validates the use of virtual reality for EEG research by comparing ERP responses in VR and traditional setups, finding comparable results and demonstrating a depth-based oddball task's effectiveness.

Adjusting ADJUST: Optimizing the ADJUST Algorithm for Pediatric Data Using Geodesic Nets

The paper optimizes the ADJUST algorithm for pediatric EEG data using geodesic nets, improving artifact classification and data retention compared to original ADJUST and ICLabel.

QUATERNIONIC ASSESSMENT OF EEG TRACES ON NERVOUS MULTIDIMENSIONAL HYPERSPHERES

The paper explores mapping three-dimensional EEG data to a four-dimensional hypersphere using quaternion projections to analyze neural activity patterns.

Sensor localization using magnetic dipole-like coils: A method for highly accurate co-registration in on-scalp MEG

This paper introduces a method for accurately localizing on-scalp MEG sensors relative to the head, improving co-registration and source localization accuracy.

Validation of melomind(TM) signal quality: a proof of concept resting-state and ERPs study

This paper validates the melomind headset's EEG signal quality by comparing it to a standard EEG system across resting-state and cognitive tasks, showing strong correlations and small differences.

Low-Density EEG for Neural Activity Reconstruction Using Multivariate Empirical Mode Decomposition

This paper improves neural activity reconstruction from low-density EEG using Multivariate Empirical Mode Decomposition (MEMD) and shows significant error reduction in both real and simulated data.

Bottom-up control of leakage in spectral electrophysiological source imaging via structured sparse bayesian learning

This paper introduces a method to control leakage in electrophysiological source imaging using structured sparse Bayesian learning and evaluates its performance against existing methods.

A Novel Performance Metric for Multiclass Subject Invariant Brain Computer Interfaces with Imbalanced Classes

The paper introduces a Q-factor metric to improve wrapper search in multiclass BCI with imbalanced classes by penalizing classifier degeneracy.

Nonlinear Interaction Decomposition (NID): A Method for Separation of Cross-Frequency Coupled Sources in Human Brain

This paper introduces NID, a method to decompose cross-frequency coupled sources in EEG/MEG data, showing it reliably extracts nonlinear interactions even at low SNR and identifies alpha-beta phase-phase coupling in resting-state EEG.

A sparse EEG-informed fMRI model for hybrid EEG-fMRI neurofeedback prediction

The paper predicts bi-modal neurofeedback scores from EEG alone using a sparse regression model, showing improved correlation with combined fMRI-EEG sessions.

Mental state space visualization for interactive modeling of personalized BCI control strategies

The paper introduces a method for visualizing mental state spaces to help users find effective BCI control strategies through interactive exploration.

Interpreting neural decoding models using grouped model reliance

This paper introduces grouped model reliance as a method to interpret neural decoding models, showing that alpha-band activity is consistently used but with inter-individual variability in frequency and topography.

Time-ResNeXt for epilepsy recognition based on EEG signals

This paper introduces Time-ResNeXt, a deep learning model for epilepsy detection from EEG signals, achieving 90.50% accuracy on a benchmark dataset.

Multinational qEEG developmental surfaces

This study analyzes multinational qEEG data to determine if country-specific developmental equations are needed, finding that age and frequency are more significant than country.

Closed-loop Neuroscience of brain rhythms: optimizing real-time quantification of narrow-band signals to expedite feedback delivery

This paper develops algorithms to reduce feedback delay in brain rhythm-contingent paradigms using EEG data from human subjects.