EEG and Brain-Computer Interfaces

A data-driven method to identify frequency boundaries in multichannel electrophysiology data

This paper introduces a data-driven method to identify frequency boundaries in electrophysiology data, showing improved precision over standard approaches in Parkinson's patients and controls.

Identifying key factors for improving ICA-based decomposition of EEG data in mobile and stationary experiments

This study evaluates how preprocessing parameters affect ICA decomposition of EEG data in mobile and stationary experiments, finding that higher high-pass filters and more channels improve results in mobile settings.

Exploring the limitations of event-related potential measures in moving subjects. Case studies of four different technical modifications in ergometer rowing

The paper tests four EEG/ERP setups for measuring brain activity during ergometer rowing, finding reliable visual evoked potentials but challenges in detecting motor-related activity due to movement artifacts.

EEG-based decoding and recognition of imagined music

The paper retracts due to a major error in analysis code affecting results on imagined music data.

Task-Free Recovery and Spatial Characterization of a P3-Like Global Network from Resting-State EEG

The paper extracts a P3-like global network from resting-state EEG using SOBI, showing reliable individual differences in neural networks associated with this component.

Topological Features of Electroencephalography are Reference-Invariant

This paper shows that topological features of EEG data are reference-invariant, while correlation spaces are not, suggesting topological analysis can capture individual brain dynamics.

Applying SCA for high-accuracy cortical auditory ERPs in children

This paper validates Spike-density Component Analysis (SCA) for isolating neural components in children's auditory ERPs, showing superior accuracy with SCA-HSAC over other methods.

Hits-Based Quantitative Characterization of SOBI-Recovered P3 Network Configuration: an EEG Source-Imaging Study

This paper quantitatively characterizes the spatial configuration of the P3 network using EEG source imaging and hits analysis, revealing frontal structures' role in novelty processing.

Minimizing biosignal recording sites for hybrid noninvasive brain/neural robot control

This paper introduces a strategy for brain/neural robot control using fewer scalp electrodes, showing comparable classification accuracy to conventional methods even when participants chew.

RAVE: comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data

RAVE is an open-source software for analyzing and visualizing intracranial EEG data, enabling users to perform custom analyses and visualize results without coding.

Decoding the covert shift of spatial attention from electroencephalographic signals permits reliable control of a brain-computer interface

This paper introduces a BCI method that decodes covert spatial attention from EEG signals, achieving reliable control without requiring eye movements or multiple stimulus presentations.

EEG Source Identification through Phase Space Reconstruction and Complex Networks

This paper proposes a hybrid method for EEG source identification and artifact removal using SOBI and angle space analysis, showing improved performance over recent studies.

Introducing Rhythmic Sinusoidal Amplitude-Modulated Auditory Stimuli with Multiple Message Frequency Coding for Fatigue Reduction in Normal Subjects: An EEG Study

This study introduces novel rhythmic auditory stimuli for BCIs, showing they improve classification accuracy and reduce user fatigue compared to simple stimuli.

EEG Artifact to Signal: Predicting Horizontal Gaze Position from SOBI-DANS Identified Ocular Artifact Components

The paper introduces a method to predict horizontal gaze position from EEG data by analyzing ocular artifact components, achieving moderate accuracy and reliability with limited data.

Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band

This study shows that mindfulness-based stress reduction improves BCI performance by enhancing control over alpha band neural activity during intentional rest.

A surface metric and software toolbox for EEG electrode grids in the macaque

The paper introduces a surface metric and software tool for standardizing EEG electrode placement on macaque skulls, enabling automated and consistent grid planning across animals.

Evaluating three different adaptive decomposition methods for EEG signal seizure detection and classification

This paper evaluates three adaptive decomposition methods for EEG seizure detection and classification, finding similar high accuracy across methods with one achieving 98.2%.

Controlling a robotic arm for functional tasks using a wireless head-joystick: A case study of a child with congenital absence of upper and lower limbs

This study demonstrates that a child with no limbs can control a robotic arm using head movements, achieving task performance comparable to a control participant using a manual joystick.

Can the occipital alpha-phase speed up visual detection through a real-time EEG-based brain-computer interface (BCI)?

This study tests if real-time EEG-based BCI can use occipital alpha-phase to improve visual detection, but finds no consistent behavioral effect.

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

This study investigates whether pre-training brain activity can predict success in real-time fMRI neurofeedback learning across 24 studies and 401 participants, finding a weak correlation but no consistent predictors.

10 Years of EPOC: A scoping review of Emotiv's portable EEG device

This paper reviews 382 studies using Emotiv EPOC EEG device across 10 years, showing its use in BCI, experimental research, and clinical applications.

On-scalp MEG SQUIDs are sensitive to early somatosensory activity unseen by conventional MEG

This study compares on-scalp and conventional MEG systems, finding that on-scalp MEG better captures early somatosensory activity, including N16 and P23 components, not typically seen in conventional MEG.

Biophysical modeling of the neural origin of EEG and MEG signals

This paper presents a method to compute current dipoles from simulated neural activity to model EEG and MEG signals with biophysical detail.

An artistic approach to neurofeedback for emotion regulation

This study explores artistic visual representations of emotions for neurofeedback, finding that particle direction and concentration were perceived as artistic and controllable by participants.

supFunSim: spatial filtering toolbox for EEG

The paper introduces a toolbox for EEG signal processing that provides spatial filters and source reconstruction methods, with a focus on modularity and extensibility.

Difference in somatosensory event-related potentials in the blind subjects leads to better performance in tactile P300 BCI

This study shows blind subjects outperform sighted controls in tactile P300 BCI tasks, with higher ERP amplitudes and lower latencies, and performance correlates with Braille reading speed.

Deep Learning for Non-Invasive Cortical Potential Imaging

This paper introduces deep learning methods to improve EEG spatial resolution by translating scalp potentials to cortical sources using CNN-based solvers trained on synthetic data.

Simultaneous MRI-EEG during a motor imagery neurofeedback task: an open access brain imaging dataset for multi-modal data integration.

This paper presents a multimodal dataset combining EEG and fMRI during a motor imagery neurofeedback task, enabling research on multi-modal data integration and neurofeedback methodologies.

Eye-movement related brain potentials during assisted navigation in real-world

The paper shows that mobile EEG can capture brain activity related to real-world navigation by extracting saccade- and blink-related potentials and gait activity, even in complex environments.

Analysis in the frequency domain of multicomponent oscillatory modes of the human electroencephalogram extracted with multivariate empirical mode decomposition.

The paper analyzes EEG data using multivariate empirical mode decomposition and compares spectral analysis methods to evaluate their performance in healthy humans.