EEG and Brain-Computer Interfaces

The paper develops a deep learning model to remove EOG artifacts from EEG data using eye-tracking data, showing that ICA has low specificity for EOG removal.

This paper decodes speech features from EEG signals using deep learning models, achieving above-chance performance for both discrete and continuous speech tasks.

The paper develops a computational framework using control and game theory to predict and optimize user-decoder interactions in co-adaptive neural interfaces through experimental validation.

The paper compares dipole source reconstruction accuracy between 3-layer and 5-layer BEM-FMM models, finding that 3-layer models provide good localization but less accurate orientation, with AMR improving 5-layer accuracy.

This paper introduces a deep neural network framework that directly predicts the complex-valued analytic signal of EEG from raw data, outperforming traditional methods and showing robustness to missing electrodes.

The paper evaluates denoising techniques for EEG data during transcutaneous auricular vagus nerve stimulation, finding that GED/SASS and SSP are most effective in reducing artifacts while preserving signal integrity.

This study uses Explainable AI to analyze uhd-EEG signals for speech decoding, showing neural contributions are distinct from EMG artifacts and can be enhanced by denoising.

The paper classifies working memory loads using Bayesian functional connectivity and graph convolutional networks, achieving 96% accuracy in intrasubject classification.

This paper introduces a convolutional neural network for accurate M/EEG source localization, validated on simulations and real data, showing improved accuracy for both cortical and subcortical regions.

This paper develops a flexible intracortical BCI for typing using finger movements, achieving high accuracy and speed in two typing paradigms.

This paper develops a theory explaining BCI learning through low-dimensional control strategies, validated with experimental data.

This paper introduces a method to infer brain activity sources and their locations from electromagnetic signals using linear analysis and anatomical models.

The paper proposes a channel selection strategy for motor imagery EEG signals using brain function networks, reducing channel count while improving recognition accuracy.

The paper shows assistive decoders in BCIs influence neural representations by concentrating task-relevant information in fewer neurons and modes.

This paper introduces a decoder that translates single-trial non-invasive fMRI recordings into continuous text, outperforming existing methods in cross-subject settings.

The paper constructs invariant features for EEG/MEG signals using Grassmann manifolds to improve the analysis of functional connectivity in brain activity.

This paper quantifies EEG heterogeneity in psychiatric disorders using normative modeling, finding significant individual deviations that correlate with clinical assessments.

This study shows that artificial neural networks produce more naturalistic finger movements than linear methods in brain-machine interfaces.

This paper uses single-channel sleep EEG features to predict health outcomes like age, sex, and disease risk using machine learning models.

This paper evaluates EEG and EMG signals to detect movement attempts in stroke patients, finding that combining both improves decoding accuracy and that EMG correlates with impairment severity.

The paper introduces the ChineseEEG dataset, a high-density EEG dataset with Chinese linguistic stimuli, for semantic alignment and neural decoding research.

This paper implements and validates the first RIFT-EEG setup, showing that RIFT can evoke measurable oscillatory responses in EEG comparable to MEG.

The paper introduces a non-invasive neurotechnology platform with multi-modal sensors and integration into BCI2000, demonstrating its use in sleep monitoring and evoked potentials.

SleepEEGpy provides a Python package for sleep EEG data preprocessing, analysis, and visualization, offering a unified workflow for researchers.

This paper introduces a denoising diffusion model for EEG signals that uses pose coordinates to improve denoising performance and maintain spatial dependencies.

The paper introduces an open-source LED display system for steady-state visual stimulation, validated for generating SSVEPs with precise frequency and phase control.

The paper evaluates the reliability of neural components from resting-state EEG data using group ICA and identifies reproducible components across multiple datasets.

This study investigates the role of cortical and subcortical brain structures in speech decoding using sEEG signals and a deep learning model, finding that both contribute to different aspects of speech prediction.

This study externally validates machine learning models for predicting continuous pain intensity from EEG data in healthy individuals, finding limited accuracy despite some classification model success.

NeuroFreq provides a MATLAB toolbox for consistent time-frequency analysis of M/EEG data, offering multiple transforms and utilities for processing and visualizing results.