EEG and Brain-Computer Interfaces

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

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.

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.

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

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

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

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

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.

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

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.

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

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

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.

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.

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.

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

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

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

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

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.

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.

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

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.

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.

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

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

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.

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.

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