EEG and Brain-Computer Interfaces

YAEL: Your Advanced Electrode Localizer

YAEL streamlines electrode localization in iEEG by integrating all steps into a single GUI, enabling efficient and automated localization with high-quality visualization.

Asynchronous Detection of Erroneous Behaviors in Human-Robot Interaction with EEG: A Comparative Analysis of Machine Learning Models.

This paper compares two machine learning models for detecting errors in human-robot interaction using EEG data, finding significant performance differences between them.

Auditory attention detection in cocktail-party: A microstate study

This paper introduces a real-time system for detecting selective auditory attention using EEG microstates, showing improved classification performance with 6 optimized microstate topographies.

Contribution of image statistics and semantics in local vs. distributed EEG decoding of rapid serial visual presentation

The paper decodes semantic content from EEG data during rapid serial visual presentation by distinguishing between image statistics and semantics, finding simpler models are more robust to statistical biases.

Automatic sleep staging by a hybrid model based on deep 1D-ResNet-SE and LSTM with single-channel raw EEG signals

This paper proposes a hybrid deep learning model for automatic sleep staging using single-channel EEG signals, achieving high accuracy and F1-scores across sleep stages.

Optimal parameters for Rapid invisible Frequency Tagging using MEG

This study identifies optimal parameters for rapid invisible frequency tagging (RFT) using MEG, showing that stimulation frequency, patch size, and position significantly affect brain responses.

DISCOVER-EEG: an open, fully automated EEG pipeline for biomarker discovery in clinical neuroscience

DISCOVER-EEG is an open, automated EEG pipeline that preprocesses, analyzes, and visualizes resting state EEG data to extract brain function features, tested on two large datasets.

The olfactory-based neurofeedback of the EEG alpha rhythm

This study develops an olfactory-based neurofeedback system using EEG alpha rhythm, showing increased alpha power in true NFB groups compared to a mock group.

A Dimensionality Reduction Approach for Motor Imagery BCI using Functional Clustering, Graph Signal Processing, and Differential Evolution

This paper introduces a dimensionality reduction approach for motor imagery BCI using functional clustering, graph signal processing, and differential evolution, achieving high classification accuracy.

Collective Almost Synchronization Modeling Used for Motor Imagery EEG Classification

This paper introduces a novel feature extraction method for Motor Imagery EEG classification using a Collective Almost Synchronization model, achieving 74.03% accuracy on a benchmark dataset.

A high-performance speech neuroprosthesis

This paper demonstrates a high-performance speech neuroprosthesis that achieves low word error rates and high decoding speeds using intracortical recordings from a person with ALS.

Validation of the sleep EEG headband ZMax

The paper validates a wearable PSG system, ZMax, comparing its sleep scoring and metrics to standard PSG, finding moderate to substantial agreement with human scorers.

Successful Modulation of Temporoparietal Junction Activity and Stimulus-Driven Attention by fNIRS-based Neurofeedback - a Randomized Controlled Proof-of-Concept Study

This study shows that fNIRS-based neurofeedback can modulate rTPJ activity and affect attention reorienting, with learning differences between up- and downregulation groups.

A Deep Learning Approach to Detecting Temporal Characteristics of Cortical Regions

The paper uses deep learning to predict electrode locations in macaque brains from ECoG signals, finding unique temporal patterns associated with different cortical regions.

Resting-state EEG recorded with gel-based versus consumer dry electrodes: spectral characteristics and across-device correlations

Bias in group-level EEG microstate analysis

This study identifies biases in group-level EEG microstate analysis and proposes a paired analysis approach to reduce errors from subgroup topography differences.

Using adversarial networks to extend brain computer interface decoding accuracy over time

This paper improves brain-computer interface decoding accuracy over time using Cycle-GAN and ADAN methods, outperforming linear methods with minimal data requirements.

EEG error-related potentials encode magnitude of errors and individual perceptual thresholds

This study shows that error-related potentials (ErrPs) encode both the magnitude of errors and individual perceptual thresholds through amplitude and latency, with implications for cerebellar contributions to performance monitoring.

Localized estimation of electromagnetic sources underlying event-related fields using recurrent neural networks

This paper uses a recurrent neural network to reconstruct neural activity from MEG data, showing improved signal quality compared to existing methods.

Post-Stimulus Encoding of Decision Confidence in EEG: Toward a Brain-Computer Interface for Decision Making

This paper investigates whether decision confidence can be decoded from EEG signals post-stimulus and pre-response to enable a brain-computer interface for improving decision making.

Crowdsourced EEG Experiments: A proof of concept for remote EEG acquisition using EmotivPRO Builder and EmotivLABS

The paper demonstrates remote EEG data collection using EmotivPRO Builder and EmotivLABS, showing a significant alpha power difference between resting state conditions.

Direct Speech Reconstruction from Sensorimotor Brain Activity with Optimized Deep Learning Models

This paper optimizes deep learning models for speech reconstruction from sensorimotor brain activity, achieving high word decoding accuracy and intelligible speech.

Quantum analysis of P3a and P3b from auditory single trial ERPs differentiates borderline personality disorder from schizophrenia

This paper applies quantum mechanics to EEG analysis, differentiating borderline personality disorder from schizophrenia by analyzing P3a and P3b components in single trial ERPs.

Principles for Optimal Window Size Selection for Infant and Adult EEG Connectivity Analysis

This paper examines optimal window size selection for EEG connectivity analysis in infants, adults, and dyadic pairs, finding that optimal sizes vary depending on the metric and dataset.

EEG Signal Processing to Control a Finger Rehabilitation System

This paper compares EEG signal classification methods for motor imagery in time and time-frequency domains using SVM and LDA with PCA, finding SVM performs best.

Evaluating the Influence of Anatomical Accuracy and Electrode Positions on EEG Forward Solutions

This study evaluates how anatomical accuracy and electrode positions affect EEG forward solutions, finding that SimNIBS provides more accurate results than MNE-Python and FieldTrip.

Is EEG best left alone?

This paper re-evaluates the benefit of EEG pre-processing using the same data as a previous study, arguing that the metric used has limited applicability and pre-processing may be critical in some cases.

Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition

This paper introduces a bimodal EEG-fMRI dataset for inner-speech recognition, collected from four participants during inner-speech tasks.

An in-vivo validation of ESI methods with focal sources

The paper compares ten ESI methods using in-vivo EEG data to evaluate their accuracy and parameter sensitivity in localizing focal brain sources.

Dynamic neural reconstructions of attended object location and features using EEG

This study uses EEG and machine learning to show how neural representations of object features and locations dynamically update during attention shifts.