EEG and Brain-Computer Interfaces

A simulation-based approach to improve decoded neurofeedback performance

This study uses offline simulations to optimize parameters for decoded neurofeedback experiments, showing they align with real performance and explain individual differences.

No Evidence for Communication in the Complete Locked-in State

This paper critiques two studies claiming to enable communication in complete locked-in state patients, showing methodological flaws and irreproducibility of their results.

Regulation of arousal via on-line neurofeedback improves human performance in a demanding sensory-motor task

This study uses real-time neurofeedback to adjust arousal levels and improve performance in a demanding sensory-motor task.

Influence of Mismarking Fiducial Locations on EEG Source Estimation

This study quantifies how fiducial location errors in EEG affect dipole source estimation and recommends accuracy thresholds to minimize uncertainty.

Multi-Scale Neural Sources of EEG: Genuine, Equivalent, and Representative. A Tutorial Review

The paper develops a biophysical framework to interpret EEG data across multiple spatial scales, categorizing sources as genuine, equivalent, or representative.

An automated, machine learning-based detection algorithm for spike-wave discharges (SWDs) in a mouse model of absence epilepsy

This paper develops an SVM-based algorithm to automatically detect and score spike-wave discharges (SWDs) in mice, showing it correlates with human confidence and behavioral state changes.

Automated Electrodes Detection during simultaneous EEG/fMRI

This paper introduces an automated method to detect EEG electrodes in MR images during simultaneous EEG/fMRI, achieving 94% detection accuracy and 3.1 mm average position error.

Real-time Detection of Driver’s Movement Intention in Response to Traffic Lights

This paper detects driver's movement intention in response to traffic lights using EEG-based decoding, showing anticipatory slow cortical potentials and single-trial classification performance.

Source reconstruction of broadband EEG/MEG data using the frequency-adaptive broadband (FAB) beamformer

This paper introduces the frequency-adaptive broadband (FAB) beamformer for EEG/MEG data, which outperforms existing methods in reconstructing broadband source signals.

Towards fast and reliable simultaneous EEG-fMRI analysis of epilepsy with automatic spike detection

This paper develops an automatic EEG spike detection method for epilepsy studies, showing it can detect more spikes, identify additional brain regions, and speed up analysis compared to manual methods.

μ-rhythm extracted with personalized EEG filters correlates with corticospinal excitability in real-time phase-triggered EEG-TMS

The paper compares two EEG filtering methods for extracting the μ-rhythm and assesses their impact on corticospinal excitability measured via TMS.

Automated Seizure Detection Tailored Towards Portable Patient Care Using A Novel, Computationally Efficient Method of Analysis

This paper develops a computationally efficient seizure detection algorithm for epilepsy management using EEG data from a mouse model, achieving 98.51% accuracy with reduced data requirements.

Analysis of EEG networks and their correlation with cognitive impairment in preschool children with epilepsy

This paper uses EEG network analysis to detect cognitive impairment in preschool children with epilepsy, achieving 85% sensitivity and 84% specificity.

Coherence-based spectro-spatial fillters for stimulus features prediction from electrocorticographic recordings.

This paper introduces a coherence-based spectro-spatial filter to predict stimulus features from electrocorticographic recordings, showing improved performance over traditional methods.

Investigating established EEG parameter during real-world driving

This study examines if P300 brain dynamics observed in lab settings are consistent in real-world driving scenarios.

A Predictive Epilepsy Index Based on Probabilistic Classification of Interictal Spike Waveforms

This paper introduces an automated method to classify and quantify interictal spikes in EEG data from mice, using probabilistic modeling to predict epileptogenic treatment.

Modelling the brain response to arbitrary visual stimulation patterns for a flexible high-speed BCI

This paper introduces a method to model brain responses to arbitrary visual stimuli using EEG, enabling flexible and high-speed brain-computer interfaces with increased information transfer rates.

Geometric Classification of Brain Network Dynamics via Conic Derivative Discriminants

This paper introduces a geometric classification method for brain network dynamics that achieves high temporal resolution and anatomical inference using conic derivative discriminants.

Towards zero-latency neurofeedback

This paper proposes an algorithm to reduce neurofeedback latency in EEG by optimizing FIR filter weights and analytic signal reconstruction, enabling predictive feedback with maintained accuracy.

Paradigm Shift in Sensorimotor Control Research and Brain Machine Interface Control: The Influence of Context on Sensorimotor representations

This paper shows that reward modulates neural activity in M1 during BMI control and demonstrates using this modulation to improve BMI decoder performance.

Human personality reflects spatio-temporal and time-frequency EEG structure

The paper uses EEG data and Schulte tables to classify human personality into three groups based on spatio-temporal and time-frequency brain activity patterns.

Developing a 3- to 6-state EEG-based brain-computer interface for a robotic manipulator control

This paper develops a noninvasive EEG-based BCI system for robotic control, showing high accuracy in motor-imagery classification and demonstrating feasibility for assistive robotic applications.

Pathways to Consumers Minds: Using Machine Learning and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

This study uses EEG and machine learning to predict consumer preferences and commercial success better than traditional questionnaires.

SEREEGA: Simulating Event-Related EEG Activity

SEREEGA generates simulated EEG data with known ground truth to evaluate signal processing and machine learning methods.

A Comparison of Temporal Response Function Estimation Methods for Auditory Attention Decoding

This study compares TRF estimation methods for decoding auditory attention from EEG data using a standardized dataset.

MEG/EEG group study with MNE: recommendations, quality assessments and best practices

The paper reanalyzes an open MEG/EEG dataset using MNE to compare different analysis strategies and provide best practices for group studies.

Increasing the Mobility of EEG Data Collection Using a Latte Panda Computer

This paper demonstrates that a Latte Panda computer can collect EEG data reliably and portably, comparable to a laptop, enabling more mobile EEG experiments.

Topographical and spectral signatures of infant and adult movement artifacts in naturalistic EEG

This study examines motion artifacts in infant and adult EEG during naturalistic activities, finding distinct topographical and spectral patterns in infants compared to adults.

Near Perfect Neural Critic from Motor Cortical Activity Toward an Autonomously Updating Brain Machine Interface

This paper demonstrates a neural critic that classifies reward value from motor cortical activity with high accuracy, aiming to improve brain machine interfaces.

Integrated analysis of anatomical and electrophysiological human intracranial data

The paper describes a protocol for integrating anatomical and electrophysiological human intracranial EEG data using an open-source toolbox, enabling rapid and reproducible analysis.