EEG and Brain-Computer Interfaces

Robin's Viewer: Using Deep-Learning Predictions to Assist EEG Annotation

Robin's Viewer integrates deep-learning predictions into an EEG annotation tool to improve accuracy and allow user inspection of model outputs.

Estimating functional EEG sources using topographical templates

The paper introduces a method to estimate functional EEG sources using topographical templates, showing it performs as well as individual source localization methods but is more cost-effective and interpretable.

Resting state EEG as a biomarker of Parkinson's disease: Influence of measurement conditions

This study evaluates the use of resting state EEG for Parkinson's disease classification, finding that fewer electrodes and open-eye conditions improve accuracy.

A Convolutional Autoencoder-based Explainable Clustering Approach for Resting-State EEG Analysis

This paper introduces a convolutional autoencoder-based clustering method for resting-state EEG data, identifying 8 EEG states in schizophrenia and linking time outside the dominant state to negative symptom severity.

Not one size fits all: influence of EEG type when training a deep neural network for interictal epileptiform discharge detection

This paper shows that deep learning models for detecting interictal epileptiform discharges in EEGs perform differently based on the type of EEG data used for training.

The effect of estimation time window length on overlap correction in EEG data

The paper shows longer estimation windows are better for EEG data analysis with overlap correction and provides an interactive app to visualize parameters.

Decoding accuracies as well as ERP amplitudes do not show between-task correlations

The paper investigates whether switching tasks in an event-related design affects ERP amplitudes and decoding accuracies, finding no between-task correlations.

Garments that measure EEG: Evaluation of an EEG sensor layer fully implemented with smart textiles

This paper evaluates a garment-based EEG system that uses smart textiles to measure EEG activity, showing comparable performance to dry EEG systems but with better comfort and lower environmental impact.

Validation of the EEG signal of the URGOnight neurofeedback device, associated with a new SMR detection method

This paper validates a low-cost EEG headband for neurofeedback and introduces a method to distinguish SMR from alpha rhythms using a single recording site.

Single-trial-based Temporal Principal Component Analysis on Extracting Event-related Potentials of Interest for an Individual Subject

The paper applies single-trial-based temporal principal component analysis to extract event-related potentials from individual subject data, showing improved consistency with fewer trials.

BCI learning phenomena can be explained by gradient-based optimization

This study shows that two BCI learning phenomena can be explained by gradient-based optimization, without requiring special mechanisms.

A scoping review on the use of consumer-grade EEG devices for research

This study reviews 916 peer-reviewed articles using consumer-grade EEG devices, highlighting their applications and future potential in neuroscience research.

Alpha suppression during prehension indicates neural motor drive inhibition

This paper shows alpha band suppression in motor areas during motor error correction, suggesting it reflects motor drive inhibition rather than cognitive load.

HArtMuT - Modeling eye and muscle contributors in neuroelectric imaging

HArtMuT improves M/EEG signal localization by modeling eye and muscle contributions, achieving comparable accuracy to brain sources.

Automatic Sleep Stage Classification with Optimized Selection of EEG Channels

This paper presents an ensemble algorithm that automatically classifies sleep stages using an optimized minimal number of EEG channels, achieving high accuracy with only 3 channels.

A Modular, Adaptive, Deep-Learning-Based Brain-VR Interface

This paper describes a modular, adaptive, deep-learning-based Brain-VR Interface that integrates motor imagery BCI control into a gamified VR scenario to enhance long-term user motivation.

A large and rich EEG dataset for modeling human visual object recognition

The paper introduces a large EEG dataset for visual object recognition and demonstrates its utility through multiple computational modeling approaches.

Online decoding of covert speech based on the passive perception of speech

This paper demonstrates an online covert speech BCI using a cross-condition machine learning model that achieves 75.3% ternary classification accuracy by leveraging spatiotemporal similarities between speech perception and covert speech.

Real-Time Brain-Machine Interface Achieves High-Velocity Prosthetic Finger Movements using a Biologically-Inspired Neural Network Decoder

This paper develops a biologically-inspired neural network decoder for real-time brain-machine interfaces, achieving faster and more natural prosthetic finger movements than existing methods.

Speech decoding from a small set of spatially segregated minimally invasive intracranial EEG electrodes with a compact and interpretable neural network

This paper decodes speech from minimally invasive EEG electrodes using a compact neural network, achieving 55-70% accuracy with few channels.

The sleep EEG envelope: a novel, neuronal firing-based human biomarker

The paper introduces the sleep EEG envelope spectrum as a neuronal firing-based biomarker, showing it reflects age and sex with high individual stability.

Decoding continuous variables from event-related potential (ERP) data with linear support vector regression (SVR) using the Decision Decoding Toolbox (DDTBOX)

This paper demonstrates the use of linear support vector regression (SVR) in the Decision Decoding Toolbox (DDTBOX) to predict continuous variables from ERP data, showing its effectiveness across various analysis parameters.

De novo brain-computer interfacing deforms manifold of populational neural activity patterns in human cerebral cortex

This study shows that de novo brain-computer interfaces (BCIs) alter the neural manifold of motor cortex activity during learning, with fixed classifiers stretching the manifold and adaptive classifiers preserving its structure.

Examining Reproducibility of EEG Schizophrenia Biomarkers Across Explainable Machine Learning Models

This study trains and compares random forest and SVM models to identify reproducible EEG biomarkers for schizophrenia using spectral features from resting state data.

Examining Effects of Schizophrenia on EEG with Explainable Deep Learning Models

This study applies explainable deep learning models to raw EEG data to diagnose schizophrenia, identifying key spectral and spatial features that align with prior research.

High-Resolution EEG Source Reconstruction with Boundary Element Fast Multipole Method Using Reciprocity Principle and TES Forward Model Matrix

The paper develops and validates a new EEG source localization algorithm using BEM-FMM, reciprocity principle, and TES forward model matrix, achieving high-resolution source reconstruction with low computational cost.

The Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED): Development and validation for use in pediatric populations

This paper develops and validates a standardized EEG preprocessing and entropy estimation pipeline called APPLESEED to reliably estimate scale-wise entropy for developmental studies.

Effects of Neurofeedback training on performance in laboratory tasks: A systematic review

This review evaluates neurofeedback's impact on task performance in healthy participants, finding limited statistically significant effects and variability in study design.

Benefits of individualized brain anatomies and EEG electrode positions for auditory cortex localization

This study compares the impact of using template versus individualized brain anatomies and EEG electrode positions on the localization accuracy of the primary auditory cortex using dSPM and sLORETA inverse solutions.

Application of rapid invisible frequency tagging for brain computer interfaces

This paper introduces a rapid invisible frequency-tagging technique for BCIs, enabling real-time control with high accuracy and reduced visual fatigue.