EEG and Brain-Computer Interfaces

This study investigates how different types of tactile friction affect EEG non-linear features and finds that non-linear indices can distinguish between static, dynamic, and no friction states.

This study decodes 39 English phonemes from neural signals in the dorsal precentral gyrus using intracortical electrode arrays, achieving 33.9% classification accuracy with a recurrent neural network.

This study shows that short-latency visual neurofeedback of parietal alpha activity enhances training effectiveness, with longer delays reducing sustained changes in alpha rhythm.

This study uses unsupervised AMICA decomposition to analyze high-density EEG data during emotion imagery, identifying distinct brain state dynamics across different emotions.

This paper introduces a method using filterbank common spatial patterns to decode auditory attention direction from EEG, achieving high accuracy on short signal segments and demonstrating adaptability to new subjects and limited channel setups.

This study uses machine learning to analyze 608 participants and identifies two factors improving neurofeedback performance: pre-training no-feedback runs and training patients over healthy participants.

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.

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.

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.

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

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.

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.

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.

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.

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

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.

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.

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

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.

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

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.

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

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.

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

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.

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

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.

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

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