EEG and Brain-Computer Interfaces

Source to sensor coupling (SoSeC) as an effective tool to localize interacting sources from EEG and MEG data

The paper introduces a method to localize interacting sources from EEG and MEG data by maximizing source-to-sensor coupling, which is faster and more statistically powerful than conventional approaches.

Error encoding in human speech motor cortex

The paper shows error detection signals in speech motor cortex during neuroprosthesis use, enabling error identification with 86% accuracy and revealing distinct patterns for different error types.

Assessing the impact of artifact correction and artifact rejection on the performance of SVM-based decoding of EEG signals

This study evaluates how artifact correction and rejection affect SVM-based EEG decoding performance, finding that their combination rarely improves results but correction may still be necessary to avoid confounds.

ezscore-f: A Set of Freely Available, Validated Sleep Stage Classifiers for Forehead EEG

The paper develops and validates artifact-aware sleep stage classifiers for forehead EEG, showing robust performance across multiple devices and sleep stages.

Towards Unified Neural Decoding with Brain Functional Network Modeling

This paper introduces MIBRAIN, a framework for interindividual neural decoding using brain functional network modeling, showing improved decoding accuracy with multi-subject data integration.

Human intention inference with a large language model can enhance brain-computer interface control: A proof-of-concept study

This paper demonstrates that integrating large language models with neural and oculomotor signals improves BCI control accuracy for selecting arbitrary posts in a social networking service.

Multilayer MEG source modelling enables depth-resolved laminar inference in humans

This paper introduces a multilayer MEG source modelling framework to enable depth-resolved laminar inference in humans, showing it aligns with known neural circuit motifs.

Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces

This paper improves multimodal EEG-fTCD brain-computer interfaces using FBCSP and envelope features, achieving high classification accuracies for motor imagery and mental rotation tasks.

The Neurocognitive efficiency score: Derivation, validation, and application of a novel combination of concurrent electrophysiological and behavioral data

The paper introduces a neurocognitive efficiency score (NES) combining reaction time and EEG data to quantify energy efficiency in cognitive tasks and demonstrates its utility across biological states.

A Plug-and-Play P300-Based BCI with Calibration-Free Application

This paper develops a calibration-free P300 BCI system with real-time performance comparable to offline benchmarks, using pre-trained models and a small electrode setup.

EEG-to-fMRI Prediction for Neurofeedback: Evaluating Regularized Regression and Clustering Approaches

The paper evaluates regularized regression and clustering approaches to predict fMRI activity from EEG data, finding that clustering improves predictability but generalization remains limited.

Shared Hierarchical Representations Explain Temporal Correspondence Between Brain Activity and Deep Neural Networks

The paper examines the temporal alignment between EEG responses and neural network activations, finding that early EEG components match low-level features and later components match semantic representations.

Stabilizing brain-computer interfaces through alignment of latent dynamics

This paper introduces NoMAD, a method that stabilizes brain-computer interfaces by aligning latent dynamics, achieving stable decoding without supervised recalibration over long periods.

Pumping Up your Predictive Power for Cognitive State Detection with the Proper GAINS

This paper introduces the SIS algorithm to improve cognitive state detection from EEG signals, achieving 29.8% better classification than traditional methods.

EEG-based Assessment of Long-Term Vigilance and Lapses of Attention using a User-Centered Frequency-Tagging Approach

This paper uses SSVEP responses to a frequency-tagging flicker to detect attention lapses during prolonged vigilance tasks, finding that SSVEP outperforms other EEG markers.

Comparative analysis of neural decoding algorithms for brain-machine interfaces

The paper compares neural decoding algorithms for brain-machine interfaces to find the optimal combination of signal processing, feature extraction, and classifiers.

Automatic diagnostics of electroencephalography pathology based on multi-domain feature fusion

This paper proposes a framework for improving EEG pathology diagnosis by fusing multi-domain features and reducing redundancy, achieving better performance on a clinical dataset.

Meta plasticity and Continual Learning: Mechanisms sub serving Brain Computer Interface Proficiency

This paper proposes a meta plasticity model to explain BCI learning and skill consolidation, showing that synaptic mechanisms operate across multiple time scales to improve control proficiency.

Identifying Explainable and Generalizable Features for MEG Decoding

This study identifies time-resolved MEG features that generalize across individuals for decoding visual stimuli, showing early transient features outperform EEG wavelet features and suggest shared neural coding mechanisms.

Effects of stimulus modality and response type on oddball stimulus discrimination using polarity-considered EEG microstate labeling

This study evaluates polarity-considered EEG microstate labeling for classifying oddball stimuli in auditory-visual tasks, showing improved classification accuracy with decision-tree models.

Spatial Dynamics and Functional Connectivity in EEG: Insights from Lexical Processing

This study uses EEG data from a silent reading task to classify word categories using machine learning, achieving 100% accuracy with spatial dynamics and 85% with functional connectivity models.

Stretchable, hair-compatible, and long-term stable wearable EEG system

This paper introduces a stretchable, hair-compatible EEG system that maintains signal integrity over long-term use and validates its performance in brain-computer interface tasks.

A wireless, scalable and modular EEG sensor network platform for unobtrusive brain recordings

This paper presents a wireless EEG sensor network platform that enables flexible, discreet, and robust multi-channel EEG recordings with reduced motion artefacts.

Speech motor cortex enables BCI cursor control and click

This paper demonstrates that neural activity from ventral motor cortex can enable high-performance cursor control and click for brain-computer interfaces.

Development and evaluation of a non-invasive brain-spine interface using transcutaneous spinal cord stimulation

This paper develops a non-invasive brain-spine interface using EEG and transcutaneous spinal cord stimulation to detect movement onset and improve motor rehabilitation.

Meta-AlignNN: A meta-learning framework for stable brain-computer interface performance across subjects, time, and tasks

This paper introduces Meta-AlignNN, a meta-learning framework that improves BCI stability across subjects, time, and tasks by leveraging neural population dynamics consistency.

Dissociating physiological ripples and epileptiform discharges with vision transformers

This paper uses vision transformers to distinguish between physiological ripples and epileptiform discharges in hippocampal recordings, achieving performance comparable to human experts.

Comparing MEG and EEG measurement set-ups for a brain--computer interface based on selective auditory attention

The paper compares MEG and EEG for auditory attention-based brain-computer interfaces, finding that EEG with optimal channel placement can achieve comparable performance to MEG.

Scalp EEG predicts intracranial brain activity in humans

The paper shows scalp EEG can predict intracranial brain activity in humans, with accuracy varying by region, depth, and frequency.

Accelerated learning of a noninvasive human brain-computer interface via manifold geometry

This study shows that leveraging the intrinsic manifold geometry of brain activity can accelerate learning of a noninvasive BCI by aligning brain activity within the manifold during task performance.