EEG and Brain-Computer Interfaces

The Impact of Task Context on Predicting Finger Movements in a Brain-Machine Interface

This study examines how task context affects brain-machine interface performance, finding that offline predictions are sensitive to context changes but online control remains stable.

Novel Approach Explains Spatio-Spectral Interactions in Raw Electroencephalogram Deep Learning Classifiers

This study introduces a novel explainability approach for rs-EEG deep learning classifiers, revealing spatio-spectral interactions in MDD diagnosis.

Graph representations of iEEG data for seizure detection with graph neural networks

The paper proposes a data processing pipeline that generates graph representations of iEEG data and evaluates their effectiveness for seizure detection using graph neural networks.

Event-Related Desynchronization induced by Tactile Imagery: an EEG Study

This study compares EEG patterns from real and imagined vibrotactile sensations on the right hand, finding similar ERD patterns in motor and somatosensory areas.

OICNet: A Neural Network for Online EEG Source Separation using Independent Component Analysis

OICNet improves real-time EEG source separation by integrating non-Gaussianity and orthogonality constraints, outperforming existing methods in accuracy and efficiency.

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder by Pretraining Deep Learning Models with Single Channel Sleep Stage Data

Successful reproduction of a large EEG study across software packages

This paper reanalyzed EEG data using three software tools to assess how preprocessing variability affects results.

Source Localization Using Recursively Applied and Projected MUSIC with Flexible Extent Estimation

This paper introduces FLEX-MUSIC, a method for localizing neural sources in M/EEG data that also estimates source extent, achieving high accuracy and efficiency.

Comparing manual and automatic artifact detection in sleep EEG recordings

This study compares manual and automatic artifact detection in sleep EEG recordings, finding that automatic methods can effectively eliminate artifact effects without requiring visual inspection.

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

This paper validates MEG source imaging by comparing it to an intracranial EEG atlas, finding that MEG accurately estimates oscillatory patterns in lateral regions but underestimates deep regions.

'High-Density-SleepCleaner': An open-source, semi-automatic artifact removal routine tailored to high-density sleep EEG

The paper introduces a semi-automatic artifact removal tool for high-density sleep EEG data, achieving high restoration rates of bad epochs through channel-wise interpolation.

EEG-LLAMAS: an open source, low latency, EEG-fMRI neurofeedback platform

EEG-LLAMAS improves real-time EEG-fMRI neurofeedback by reducing BCG artifacts with low latency, enabling closed-loop experiments previously difficult to perform.

Seizure dynamotype classification using non-invasive recordings

This paper classifies seizure dynamotypes from surface EEG using visual and automated methods, finding bifurcation prevalence aligns with intracranial data and varies by clinical factors.

Recording brain activity with ear-EEG (cEEGrids)

This paper provides a protocol for setting up and recording high-quality ear-EEG data using cEEGrids for extended periods in and beyond the lab.

The best of two worlds: Decoding and source-reconstructing M/EEG oscillatory activity with a unique model

This paper introduces a method combining CSP and beamformer source reconstruction for decoding M/EEG oscillatory activity, achieving performance comparable to source space decoding with lower computational cost.

Decoding Study-Independent Mind-Wandering from EEG using Convolutional Neural Networks

This paper trains study-independent mind-wandering classifiers from EEG using CNNs, finding limited generalizability but improved performance with single-trial ERP features.

A Framework for Systematically Evaluating the Representations Learned by A Deep Learning Classifier from Raw Multi-Channel Electroencephalogram Data

This paper introduces a framework for evaluating deep learning classifiers on raw EEG data, focusing on spatial, spectral, and temporal explainability, and applies it to MDD diagnosis.

Interpretable full-epoch multiclass decoding for M/EEG

This paper introduces a multiclass full-epoch decoding approach for M/EEG data that improves accuracy over sliding window methods while enabling interpretation of spatiotemporal features.

Get a new perspective on EEG: Convolutional neural network encoders for parametric t-SNE.

This paper develops CNN encoders for parametric t-SNE to analyze EEG data, producing low-dimensional representations that capture EEG characteristics and generalize to new data.

An accessible and versatile deep learning-based sleep stage classifier

The paper introduces an open-source deep learning classifier for sleep staging that achieves high accuracy and is easy to use across various electrode setups.

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 1: Algorithm and Application to Oscillations

RELAX is an automated EEG preprocessing pipeline that effectively reduces artifacts while preserving neural signals, outperforming existing methods in blink and muscle artifact removal.

Introducing RELAX (the Reduction of Electroencephalographic Artifacts): A fully automated pre-processing pipeline for cleaning EEG data - Part 2: Application to Event-Related Potentials

This paper evaluates the RELAX pipeline for cleaning EEG data before ERP analysis, finding it effective and reliable for artifact reduction and ERP estimation.

EEG is better left alone

The paper evaluates EEG preprocessing methods and finds that most automated corrections do not improve ERP significance, with only one pipeline performing better than simple high-pass filtering.

Trial Averaging for Deep EEG Classification

This study shows that trial averaging improves EEG classification performance, especially when applied to both training and testing data, outperforming Mixup in some cases.

Repetitive execution of a reach-and-lift task is associated with inter-trial changes in movement-related EEG features and their predictive power

The paper examines changes in EEG features during repetitive motor tasks and their impact on BMI performance.

Long-range alpha-synchronisation as control signal for BCI: A feasibility study

This study investigates whether long-range alpha-synchronisation can serve as a control signal for BCI by analyzing EEG data during a covert attention task, finding it unreliable at the single-trial level.

Variations in ERP Data Quality Across Paradigms, Participants, and Scoring Procedures

The paper assesses ERP data quality across paradigms, participants, and scoring procedures using the SME metric, finding large variations in quality and highlighting the impact of quantification algorithms.

Long-term unsupervised recalibration of cursor BCIs

This paper introduces a hidden Markov model for unsupervised recalibration of intracortical brain-computer interfaces, showing improved performance over long-term use.

The Nested Hierarchy of Overt, Mouthed, and Imagined Speech Activity Evident in Intracranial Recordings

The paper analyzes intracranial recordings to compare speech activity detection across overt, mouthed, and imagined speech, finding a nested hierarchy of relevant brain channels.

Stimulus-induced narrow-band gamma oscillations in humans can be recorded using open-hardware low-cost EEG amplifier

This study shows low-cost open-source EEG amplifiers can detect stimulus-induced gamma oscillations in humans, comparable to research-grade equipment.