EEG and Brain-Computer Interfaces

This paper introduces a method to model brain responses to arbitrary visual stimuli using EEG, enabling flexible and high-speed brain-computer interfaces with increased information transfer rates.

This paper introduces a geometric classification method for brain network dynamics that achieves high temporal resolution and anatomical inference using conic derivative discriminants.

This paper shows that reward modulates neural activity in M1 during BMI control and demonstrates using this modulation to improve BMI decoder performance.

The paper uses EEG data and Schulte tables to classify human personality into three groups based on spatio-temporal and time-frequency brain activity patterns.

This paper develops a noninvasive EEG-based BCI system for robotic control, showing high accuracy in motor-imagery classification and demonstrating feasibility for assistive robotic applications.

SEREEGA generates simulated EEG data with known ground truth to evaluate signal processing and machine learning methods.

This study compares TRF estimation methods for decoding auditory attention from EEG data using a standardized dataset.

The paper reanalyzes an open MEG/EEG dataset using MNE to compare different analysis strategies and provide best practices for group studies.

This paper demonstrates that a Latte Panda computer can collect EEG data reliably and portably, comparable to a laptop, enabling more mobile EEG experiments.

This paper demonstrates a neural critic that classifies reward value from motor cortical activity with high accuracy, aiming to improve brain machine interfaces.

The paper describes a protocol for integrating anatomical and electrophysiological human intracranial EEG data using an open-source toolbox, enabling rapid and reproducible analysis.

This study compares methods for inter-subject decoding of motor imagery from MEG and EEG, finding that multi-task learning with logistic regression and l2,1-norm regularization achieves the best accuracy when trained on motor imagery data.

This paper challenges bipolar referencing in intracranial EEG and proposes ICA as a more effective method for isolating local brain activity.

DDTBOX is an open-source toolbox for multivariate pattern analysis of EEG data, enabling classification and regression of experimental variables through spatiotemporal decoding.

The paper introduces techniques for preprocessing multichannel data to remove artifacts like drift, glitches, and ringing, improving data quality without discarding corrupted trials.

This study characterizes the ballistocardiogram (BCG) artifact in EEG signals during simultaneous fMRI, identifying its temporal and spatial dynamics and proposing insights for improved artifact removal.

This paper introduces a single-sensor automatic sleep-stage classification method using cross-frequency coupling estimates, achieving high accuracy and outperforming previous studies.

This paper introduces Canonical Correlation Analysis (CCA) to decode auditory brain responses and improve stimulus-response mapping for BCI applications.

This study measures auditory P3 during outdoor cycling using mobile EEG, finding similar ERP topography and morphology but with reduced amplitude and changes in P2 and N1 components.

This paper introduces a new decoding method called distribution alignment decoding (DAD) that uses movement statistics to map neural activity to motor variables, achieving comparable or better performance than traditional supervised decoders in some cases.

This paper compares MEG source inversion algorithms and fit metrics to determine the laminar origin of neural activity, finding that sparsity-constrained methods and specific fit metrics improve discrimination accuracy.

This study compares high-density EEG (SND) with standard density (ND) in capturing spatiotemporal aspects of early visual processing, finding SND provides more accurate and detailed neural information.

The paper derives and corrects analytical formulas for scalp potentials in the four-sphere EEG head model and compares them with FEM simulations.

This paper introduces a complexity-entropy map method to analyze EEG data from patients on psychotropic drugs, showing it can detect subtle changes in clinical response to treatment.

This paper introduces a spatiotemporal source separation method to improve signal-to-noise ratio and reduce dimensionality in multichannel neural data without using sinusoidal filters.

This study compares multivariate pattern analysis of MEG and EEG data to assess their representational structures in time and space, finding convergent and unique insights into visual processing.

This paper introduces a multichannel sleep spindle detection method using sparse low-rank optimization, achieving F1 scores of 0.66 and 0.62 on two databases.

This paper introduces M-CCA for aligning MEG datasets across subjects by analyzing sensor recordings directly, showing improved performance over traditional methods.

This paper shows that multivariate EEG analysis can track feature-based attentional selection with high spatial and temporal precision, outperforming standard N2pc methods.

This study shows that single-channel in-Ear-EEG can predict auditory attention focus in concurrent listening tasks.