Arnaud Delorme A

EMG-BIDS: an extension to the Brain Imaging Data Structure for electromyography

EMG-BIDS standardizes EMG data organization, enabling FAIR data sharing and supporting machine learning applications.

Modality-specific predictive templates in pre-stimulus EEG activity

This paper investigates whether pre-stimulus EEG activity can predict expected stimuli and decision strategies in both cued and uncued trials, finding that anticipation occurs even without explicit cues.

The Lab Streaming Layer for Synchronized Multimodal Recording

The paper introduces the Lab Streaming Layer (LSL) framework for synchronizing multimodal data streams across devices and software, ensuring precise timing and reliable data recording.

HBN-EEG: The FAIR implementation of the Healthy Brain Network (HBN) electroencephalography dataset

The paper presents HBN-EEG, a FAIR-formatted EEG dataset from the Healthy Brain Network project, including behavioral annotations and task data for over 2,600 participants.

BrainBeats, an Open-Source EEGLAB Plugin to Jointly Analyze EEG and Cardiovascular Signals

BrainBeats is an open-source EEGLAB plugin that jointly analyzes EEG and cardiovascular signals using three protocols for brain-heart interplay research.

Identifying good practices for detecting inter-regional linear functional connectivity from EEG

This study evaluates methods for detecting inter-regional linear functional connectivity from EEG, finding that certain pipelines outperform others and recommends best practices.

Accounting for endogenous effects in decision-making with a non-linear diffusion decision model

This paper introduces a non-linear Drift-Diffusion Model (nl-DDM) that better captures inter-trial dynamics and endogenous influences in decision-making compared to the standard DDM.

EEG is better left alone

The paper evaluates EEG preprocessing methods and finds that most automated corrections do not improve ERP significance, with only one pipeline performing better than simple high-pass filtering.

ElectroEncephaloGraphy robust statistical linear modelling using a single weight per trial

The paper introduces a method to reduce outlier influence in EEG data using principal component projection weights in linear models, improving statistical power and controlling error rates.

Validating the wearable MUSE headset for EEG spectral analysis and Frontal Alpha Asymmetry

The paper validates a low-cost wearable EEG system for measuring EEG spectral features comparable to a high-density system, showing reliable alpha power and asymmetry measurements.

Capturing the nature of events and event context using Hierarchical Event Descriptors (HED)

The paper introduces HED for event annotation in neuroimaging data, demonstrating its application to a BIDS dataset and providing best practices for event design and annotation.

Everyone Can Meditate: Characterizing a Personalized Connectomic State Space among Meditation Groups and Non-meditators

The paper uses Thought Chart to analyze functional brain connectivity differences between meditation groups and non-meditators during specific tasks.