Preliminary analysis with classical machine learning algorithms (CSP+SVM and CSP+LDA) demonstrated an average classification accuracy of about 85%-86% between the two MI paradigms; the authors anticipate the dataset will facilitate research on MI-BCI paradigms and neuroplasticity for stroke. PMID 42533005, DOI 10.1038/s41597-026-07787-y
A Multi-Paradigm Longitudinal EEG Dataset Including 'Sixth-Finger' and 'Affected-Hand' Motor Imagery of Stroke Patients
Summary
Researchers released a multi-paradigm longitudinal EEG dataset from 24 stroke patients, covering a novel 'sixth-finger' motor imagery paradigm and affected-hand motor imagery. The dataset spans the full pre-training, post-training and follow-up stages and includes raw EEG, preprocessed data and patient clinical information. Preliminary analysis with classical classifiers (CSP+SVM, CSP+LDA) kept average cross-paradigm classification accuracy at roughly 85%–86%.
Why it matters
A longitudinal, multi-stage stroke dataset — including an unusual 'sixth-finger' paradigm — fills the data gap that has kept motor-imagery BCIs and neuroplasticity studies short of real rehabilitation-trajectory evidence.
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pubmed.ncbi.nlm.nih.gov 2026-07-31
Tianjin Medical UniversityTianjin International Joint Academy of BiomedicineTianjin UniversityTianjin Huanhu HospitalNeurorehabilitationMotor DecodingChinaSwitzerlandUnited States