EEG and Brain-Computer Interfaces

A universal, open-source, high-performance tool for automated sleep staging

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.

A closed-loop stimulation approach with real-time estimation of the instantaneous phase of neural oscillations by a Kalman filter

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.

Signal complexity indicators of health status in clinical-EEG

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.

Measurements of EEG Alpha Peak Frequencies Over the Lifespan: Validating Target Ranges on an In-Clinic Platform

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.

Volitional Control of Individual Neurons in the Human Brain

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: Accurate Clustering-based Ensemble Methodto Predict Motor Imagery Tasks from Multi-channelEEG Data

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

Bayesian MEG time courses with fMRI priors

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

Alpha activity neuromodulation induced by individual alpha-based neurofeedback learning in ecological context: A double-blind randomized study

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

Hybrid EEG-EMG system to detect steering actions in car driving settings

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.

Characteristic changes in EEG spectral powers of patients with opioid-use disorder as compared with those with methamphetamine- and alcohol-use disorders

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.

Spectral Separation of Evoked and Background Cortical Activity - Application to Short Latency Median Nerve Evoked Potentials

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.

Testing covariance models for MEG source reconstruction of hippocampal activity

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

Time series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattented listening and frequency-modulated tones classification

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.

Epileptic Spike Detection Using Neural Networks with Linear-Phase Convolutions

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.

Dynamic selective auditory attention detection using RNN and reinforcement learning

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

Granger Causality Inference in EEG Source Connectivity Analysis: A State-Space Approach

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.

Auditory attention detection with EEG channel attention

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.

Don't think, feel: basal ganglia-cortical connectivity underlies self-regulation of brain oscillations in humans

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.

Heart rate variability predicts decline in sensorimotor rhythm control

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

HAPPILEE: The Harvard Automated Processing Pipeline In Low Electrode Electroencephalography, a standardized software for low density EEG and ERP data

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

A multimodal AI system for out-of-distribution generalization of seizure detection

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

Unification of Sparse Bayesian Learning Algorithms for Electromagnetic Brain Imaging with the Majorization Minimization Framework

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.

EEG-Based Focus Estimation Using Neurable's Enten Headphones and Analytics Platform

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

EEG-based neurofeedback with network components extraction: a data-driven approach by multilayer ICA extension and simultaneous EEG-fMRI measurements

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.

EEG in game user analysis: A framework for expertise classification during gameplay

This paper presents a framework using EEG signals to classify video game player expertise levels, achieving 98.04% accuracy with K-nearest neighbor classifier.

The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

This study compares a machine learning and a deep learning approach for classifying motor imagery EEG signals, finding that deep learning improves classification accuracy, especially for low performers.

Time-resolved multivariate pattern analysis of infant EEG data

The paper provides code and a pipeline for time-resolved multivariate pattern analysis of infant EEG data using linear SVM classification, showing reliable above-chance accuracy in both infants and adults.

Stopping vs Resting state during motor imagery paradigm

The paper investigates the difference between stopping motor imagery (MI) and resting states, finding that MI termination can be detected faster and more accurately using Beta band features.

ConvDip: A convolutional neural network for better M/EEG Source Imaging

ConvDip is a convolutional neural network that solves the EEG inverse problem using a distributed dipole model, outperforming existing methods in accuracy and efficiency.

Data-driven beamforming techniques to attenuate ballistocardiogram (BCG) artefacts in EEG-fMRI without detecting cardiac pulses in electrocardiography (ECG) recordings

This paper introduces a data-driven beamforming technique to reduce BCG artefacts in EEG-fMRI without using ECG signals, improving signal recovery for task-based neural activity.