Rebeca Pardo García: Our Open EEG Dataset on Motor Recovery After Chronic Stroke Is Live Now
Rebeca Pardo García, MPR at French Red Cross, shared on LinkedIn about a recent article she and her colleagues co-authored, published in Zenodo, adding:
”I’m happy to share our latest open dataset now available on Zenodo:
‘Mu and Beta Oscillatory Changes during a Motor Task following Rehabilitation in Chronic MCA Stroke: Insights from EEG’
This dataset includes EEG recordings (64 channels, 1000 Hz) and clinical data from chronic stroke patients and healthy controls, collected before and after a rehabilitation intervention.
What you’ll find:
- Resting-state and task-related EEG (motor imagery and execution)
- Preprocessed data (EEGLAB / ERPLAB / MATLAB pipelines)
- qEEG metrics (spectral power, DAR, DTABR, BSI)
- ERD/ERS and ERP analyses
- Reproducible MATLAB code + step-by-step preprocessing guide
Our goal is to support open science and reproducibility in neurorehabilitation research, and to facilitate further exploration of EEG biomarkers of recovery after stroke.
This dataset may be useful for researchers working on:
- Neuroplasticity and stroke recovery
- Brain-computer interfaces (BCI)
- Motor control and rehabilitation
- EEG signal processing
We’d love to see how the community builds on this work – feedback, collaborations, and reuse are very welcome!”
Title: Mu and Beta Oscillatory Changes during a motor task following Rehabilitation in Chronic MCA Stroke: Insights from EEG
Authors: Rebeca Pardo-Garcia, María Ruiz-Izquierdo, Mercedes Garcia de la Vega, Rocio Calvillo, George Kontaxakis, Eva M. Moreno, M. A. Pozo
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