EEG and Brain-Computer Interfaces

Using Multivariate Pattern Analysis to Increase Effect Sizes for Event-Related Potential Analyses

The paper applies multivariate pattern analysis to ERP data to increase effect sizes and statistical power compared to traditional univariate methods.

Assistive sensory-motor perturbations influence learned neural representations

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

Eye-movement artifact correction in infant EEG

This study compares ICA and Artifact Blocking for correcting eye-movement artifacts in infant EEG, finding ICA more sensitive but less specific than AB.

Decoding Continuous Character-based Language from Non-invasive Brain Recordings

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

Characterization of second-order mixing effects in reconstructed cross-spectra of random neural fields

The paper characterizes second-order mixing effects in EEG/MEG cross-spectra and derives properties of signal mixing using resolution operators and cross-talk functions.

Construction of invariant features for time-domain EEG/MEG signals using Grassmann manifolds

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

Beyond homogeneity: Charting the landscape of heterogeneity in psychiatric electroencephalography

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

Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods

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

Deep EEG Source Localization via EMD-based fMRI High Spatial Frequency

This study improves EEG source localization by using high spatial-frequency components from fMRI, showing better performance than traditional methods.

SlumberNet: Deep learning classification of sleep stages using residual neural networks

SlumberNet is a deep learning model that classifies sleep stages in mice with high accuracy and speed, showing potential for adaptation to human sleep analysis.

On Monitoring Brain Health from the Depths of Sleep: Feature Engineering and Machine Learning Insights for Digital Biomarker Development

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

Flexible Multi-Step Hypothesis Testing of Human ECoG Data using Cluster-based Permutation Tests with GLMEs

This paper introduces a flexible multi-step hypothesis testing method using cluster-based permutation tests with LMEs and GLMEs to analyze human ECoG data, improving statistical power and accounting for random effects.

Measurement of the mapping between intracranial EEG and fMRI recordings in the human brain

The paper examines the relationship between intracranial EEG and fMRI in the sensorimotor cortex, finding that high gamma power correlates with fMRI changes during motor tasks and rest.

Unveiling Movement Intention after Stroke: Integrating EEG and EMG for Motor Rehabilitation

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.

ODL-BCI: Optimal deep learning model for brain-computer interface to classify students confusion via hyperparameter tuning

This paper introduces an optimized deep learning model for real-time classification of student confusion using EEG data, achieving higher accuracy than existing methods.

ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding

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

Rapid Invisible Frequency Tagging (RIFT) in a novel setup with EEG

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

Optimal Filters for ERP Research I: A General Approach for Selecting Filter Settings

The paper develops a method to select optimal filter settings for ERP data by maximizing signal-to-noise ratio while minimizing waveform distortion.

Optimal Filters for ERP Research II: Recommended Settings for Seven Common ERP Components

The paper recommends optimal filter cutoffs for seven common ERP components to balance signal-to-noise ratio and waveform distortion across different noise levels.

What are we really decoding? Unveiling biases in EEG-based decoding of the spatial focus of auditory attention

This study investigates biases in EEG-based spatial auditory attention decoding, finding that simple models can overfit to eye-gaze and trial-specific confounds, leading to inflated accuracy.

Translating deep learning to neuroprosthetic control

This paper shows that recurrent neural networks (RNNs) can be adapted for real-time neuroprosthetic control by modifying their training data's temporal structure, enabling better performance than standard linear methods.

Beta bursts question the ruling power for brain-computer interfaces

The paper investigates beta burst activity in motor imagery tasks, showing that burst-based features can match or exceed traditional beta power methods in decoding brain signals.

Evaluating the Effectiveness of Artifact Correction and Rejection in Event-related Potential Research

This paper evaluates artifact correction and rejection methods in ERP research, finding that correction reduces confounds and rejection reduces noise, improving data quality.

Surfing beta burst waveforms to improve motor imagery-based BCI

This paper improves motor imagery-based BCI by analyzing beta burst waveforms instead of beta power, showing better classification performance and information transfer rates.

Speech recognition from MEG data using covariance filters

This paper develops a method for speech recognition from MEG data using covariance filters, achieving reliable recognition with minimal errors in 7 subjects.

Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces

This study compares KalmanNet, an explainable BMI decoder, with deep-learning and Kalman filter models, finding it achieves comparable performance while offering interpretability.

Decoding Internal Decision Making During Reverse Engineering Tasks

This paper develops a method to decode internal decision making during reverse engineering tasks using neural markers and user behavior metrics.

Trial-by-trial detection of cognitive events in neural time-series

This paper introduces a method to detect trial-wise cognitive events in neural time-series, showing it can estimate event patterns, sensor contributions, and inter-event intervals from EEG/MEG data.

Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG

This study predicts age from at-home EEG data during sleep and meditation, achieving R2 scores of 0.3-0.5, comparable to lab-grade EEG benchmarks.

The GLM-Spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling

The paper introduces the GLM-Spectrum framework for analyzing power spectra with confound and covariate modeling, showing its ability to quantify condition differences and age effects in EEG data.