EEG and Brain-Computer Interfaces

Frequency-tagging of spatial attention using periliminal flickers

This study uses periliminal and subliminal flickers to measure spatial attention via SSVEP, showing improved user experience and classification accuracy.

Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures

This study introduces two novel convolutional neural networks for MDD diagnosis from EEG data, finding higher beta and delta power and right hemisphere correlation in MDD patients.

A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings

The paper develops a supervised spatial filter to remove speech artifacts from intracranial EEG recordings while preserving neural activity.

Towards the automated detection of interictal epileptiform discharges with magnetoencephalography

This paper develops automated methods using ICA and HMM to detect and localize interictal epileptiform discharges in MEG data, showing high sensitivity and potential to improve clinical practice.

Two common issues in synchronized multimodal recordings with EEG: Jitter and Latency

The paper addresses two common issues in synchronized multimodal EEG recordings: jitter and latency, using a Lab Streaming Layer-based system for precise time alignment.

SleepEEGpy: a Python-based software 'wrapper' package to organize preprocessing, analysis, and visualization of sleep EEG data

SleepEEGpy provides a Python package for sleep EEG data preprocessing, analysis, and visualization, offering a unified workflow for researchers.

Measuring Neuronal Avalanches to inform Brain-Computer Interfaces

This study uses neuronal avalanches to improve brain-computer interfaces by showing their task-specific differences can be decoded better than current methods.

A Pose-Informed De-Noising Diffusion Model for Adult Naturalistic EEG Signals

This paper introduces a denoising diffusion model for EEG signals that uses pose coordinates to improve denoising performance and maintain spatial dependencies.

Single-Channel EEG Artifact Identification with the Spectral Slope

This study introduces a single-channel EEG artifact detection method using spectral slope, achieving high accuracy in identifying contaminated epochs in rat recordings.

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage Data

This study improves major depressive disorder diagnosis using raw multichannel EEG with transfer learning from single-channel sleep stage data, achieving better performance than previous methods.

An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-based Schizophrenia Diagnosis

This study develops a robust and explainable deep learning model for schizophrenia diagnosis using EEG data, showing improved performance and prioritization of specific brain regions and frequency bands.

Efficacy of neurofeedback training for improving attentional performance in healthy adults: A systematic review and meta-analysis

This meta-analysis evaluates neurofeedback training's impact on attention in healthy adults, finding a significant overall effect but no difference from sham training.

A High-Resolution LED Display for Steady State Visual Stimulation: Customized, Affordable, and Open Source

The paper introduces an open-source LED display system for steady-state visual stimulation, validated for generating SSVEPs with precise frequency and phase control.

Decoding Motor Plans Using a Closed-Loop Ultrasonic Brain-Machine Interface

This study demonstrates a closed-loop ultrasonic brain-machine interface that enables nonhuman primates to control movements using real-time neurovascular activity predictions.

Harmonizing and aligning M/EEG datasets with covariance-based techniques to enhance predictive regression modeling

This paper improves M/EEG-based regression models by applying covariance-based domain adaptation techniques to address dataset shifts across different recording contexts.

Reproducible Neuronal Components found using Group Independent Component Analysis in Resting State Electroencephalographic Data

The paper evaluates the reliability of neural components from resting-state EEG data using group ICA and identifies reproducible components across multiple datasets.

Investigation of contributions from cortical and subcortical brain structures for speech decoding

This study investigates the role of cortical and subcortical brain structures in speech decoding using sEEG signals and a deep learning model, finding that both contribute to different aspects of speech prediction.

Mental image reconstruction from human brain activity

This study improves image reconstruction from brain activity by incorporating semantic information, enabling reconstruction of both seen and imagined images.

External Validation of Machine Learning and EEG for Continuous Pain Intensity Prediction in Healthy Individuals

This study externally validates machine learning models for predicting continuous pain intensity from EEG data in healthy individuals, finding limited accuracy despite some classification model success.

Burst c-VEP Based BCI: Optimizing Stimulus Design for Enhanced Classification with Minimal Calibration Data and Improved User Experience

This paper introduces Burst c-VEP, a novel visual stimulus design for BCI that improves classification accuracy and user experience with minimal calibration data.

Inter-brain coupling analysis reveals learning-related attention of primary school students

This paper introduces an inter-brain attention coupling method to detect learning-related attention in primary school students and finds it correlates with academic performance.

NeuroFreq: a MATLAB Toolbox for Time-Frequency Analysis of M/EEG Data

NeuroFreq provides a MATLAB toolbox for consistent time-frequency analysis of M/EEG data, offering multiple transforms and utilities for processing and visualizing results.

Semi-automated seizure detection using interpretable machine learning models

The paper develops a semi-automated seizure detection system using interpretable machine learning models and an open-source tool for improved efficiency and accuracy.

Alpha and SSVEP power outperform Gamma power in capturing Attentional Modulation in Human EEG

This study compares alpha, SSVEP, and gamma power in human EEG to determine their effectiveness in capturing attentional modulation, finding alpha and SSVEP are comparable while gamma is less effective for non-foveal stimuli.

SiamEEGNet: Siamese Neural Network-Based EEG Decoding for Drowsiness Detection

SiamEEGNet uses a Siamese neural network to detect drowsiness by analyzing relative EEG changes between baseline and time windows, outperforming existing methods in cross-subject scenarios.

Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum

The paper compares neural representations in motor cortex, prefrontal cortex, and striatum during brain-machine interface and manual reaching tasks in nonhuman primates.

Spectral Representation of EEG Data using Learned Graphs with Application to Motor Imagery Decoding

This paper introduces a method to transform EEG data into a spectral representation using learned graphs for motor imagery decoding, showing improved performance over existing methods.

Monkey-to-human transfer of brain computer interface decoders

This paper transfers brain-computer interface decoders from monkeys to humans, showing that motor-related neural representations are consistent across species and reducing the need for extensive data.

A scalable and robust system for Audience EEG recordings

The paper presents a scalable EEG hyper-scanning system for recording audience brain activity using low-cost components and custom software, achieving robust data alignment and performance in both lab and cinema settings.

Feature Engineering for an Efficient Motor RelatedEcoG BCI System

This study improves motor-related ECoG BCI systems by using genetic feature selection and signal splitting to enhance accuracy and reduce prediction time.