EEG and Brain-Computer Interfaces

This study classifies sleep stages using multimodal electrophysiology data and introduces an ablation method to assess modality importance, finding EEG is most important for most stages and EOG for REM and nonREM.

This study develops a brain-machine interface for virtual self-motion by decoding neural activity from monkeys performing non-reaching arm movements, showing that different movement classes require distinct decoding strategies.

The paper validates age-stratified target ranges for P300 metrics using clinical data and compares them to research trends, showing consistency in age-related changes.

This paper benchmarks EEG artifact removal methods using a novel dataset and evaluates the effectiveness of Independent Component Analysis (ICA) in isolating artifacts.

The paper adapts an adult ICA component classifier for infant EEG data, showing improved performance in artifact removal and classification accuracy compared to both the original adult classifier and manual methods.

This paper introduces a kernel-based nonlinear manifold learning method for EEG channel selection to distinguish Alzheimer's disease patients from healthy controls.

This paper introduces an automated sleep staging tool with high accuracy, validated on diverse populations, and provides an open-source, free, and user-friendly solution.

This paper introduces a Kalman filter-based method for real-time estimation of EEG oscillatory phase to enable closed-loop sensory stimulation, showing higher accuracy than autoregressive models.

This study examines multiscale entropy (MSE) in clinical-EEG data to assess its ability to distinguish health status across neurological disorders and non-brain comorbidities.

The paper validates in-clinic EEG alpha peak frequency measurements across age groups and confirms their stability over time, supporting their use for tracking cognitive changes.

This study shows humans can volitionally control single neuron firing rates in memory-related brain structures through neuroprosthetic training, with associated changes in neural activity patterns.

CluSem improves MI-EEG classification accuracy by 5-15% using a clustering-based ensemble method and is evaluated in a brain game called CluGame.

This study uses fMRI spatial priors in a Bayesian framework to improve MEG source reconstruction accuracy using simulated and experimental data.

This study investigates alpha activity neuromodulation through neurofeedback training in a double-blind randomized study, finding increased alpha activity only in the neurofeedback group.

The paper presents a hybrid EEG-EMG system to detect left and right steering actions in driving scenarios, showing improved performance through cross-correlation analysis.

The paper compares EEG spectral power changes in patients with opioid-use disorder to those with methamphetamine and alcohol-use disorders, finding significant differences across multiple analytical perspectives.

This paper introduces N-interval Fourier Transform Analysis (N-FTA) to separate evoked and background activity in neural signals, identifying clinically relevant spectral bands in short latency median nerve evoked potentials.

The paper tests covariance models for MEG source reconstruction, finding that incorporating a correlated hippocampal source model improves model evidence.

The paper analyzes time series variability of MEG power spectra during different cognitive states and finds that dominant frequency spectra vary as weakly stationary processes.

This paper introduces data-driven neural networks with linear-phase filters for detecting epileptic spikes in EEG data, achieving an average AUC of 0.967.

This paper develops a dynamic selective auditory attention detection system using RNN and reinforcement learning, achieving 94.2% classification accuracy with minimal delay.

This paper proposes a state-space model for estimating brain effective connectivity from EEG signals using Granger causality, showing improved performance over existing methods in simulated and real data.

This paper introduces a soft channel attention mechanism for auditory attention detection using EEG signals, achieving high accuracy across different channel counts and decision window sizes.

The paper investigates how humans self-regulate cortical oscillations during brain-computer interface tasks, finding that basal ganglia-cortical connectivity plays a key role in this process.

This study shows that heart rate variability predicts decline in BCI control performance during voluntary sensorimotor rhythm control in healthy participants.

HAPPILEE is a standardized automated pipeline for low-density EEG and ERP data processing, optimized for scalability and quality metrics.

This paper develops a multimodal AI system combining EEG and ECG for seizure detection, showing improved performance across out-of-distribution clinical datasets.

This paper unifies three sparse Bayesian learning algorithms for EEG/MEG brain source imaging under the majorization-minimization framework and introduces a new method for low SNR settings and noise variance estimation.

The paper evaluates an EEG-based focus detection system using Neurables Enten headphones and shows it accurately captures distractions across sessions and participants.

This paper develops an EEG-based neurofeedback system using SPLICE for depression treatment, showing reduced rumination in a NF group but no direct symptom reduction link.