New Dataset to Advance Stroke Recovery Research
A team of researchers from Skoltech, the Federal Center for Brain and Neurotechnologies (FMBA of Russia) and Lomonosov Moscow State University has released a novel dataset designed to accelerate research into stroke recovery. Published in Scientific Data on March 3, 2026, the dataset uniquely combines long-term recordings of brain activity obtained through electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS).
Understanding Stroke and the Need for Advanced Rehabilitation
Stroke remains a leading cause of disability globally, with motor impairments significantly impacting patients’ quality of life. Effective rehabilitation hinges on a detailed understanding of how the brain regains control over movement. Assessing cerebral blood flow is crucial, as stroke fundamentally involves disruption of the brain’s blood supply.
The Power of Combining EEG and fNIRS
Functional near-infrared spectroscopy (fNIRS) is a non-invasive technique that uses infrared light to measure changes in oxygenated and deoxygenated hemoglobin levels in the brain. Unlike functional MRI, fNIRS equipment is portable, affordable, and allows for continuous monitoring of patients during rehabilitation sessions.
Electroencephalography (EEG) has long been used in stroke prognosis and rehabilitation, but the precise changes in sensorimotor rhythms and cortical potentials during attempted movement have remained unclear. This limits its clinical application and the development of brain-computer interfaces to aid recovery.
A Comprehensive View of Brain Recovery
“We combined EEG and fNIRS to obtain a more comprehensive picture. EEG captures fast electrical activity of neurons, while fNIRS shows how blood vessels respond—where blood flows, where the brain consumes more oxygen. This is a slower but equally important process. Together, the two methods provide a fuller understanding of how the brain recovers and allow us to study neurovascular coupling—how neuronal activity relates to blood flow,” explained Junior Research Scientist Alexandra Medvedeva at the Neuro Center of Skoltech.
Study Details and Data Availability
The study involved 16 patients with hemiparesis (partial weakness on one side of the body), aged 42 to 71, who participated in 84 rehabilitation sessions at the Federal Center for Brain, and Neurotechnologies. All data – including fNIRS and EEG signals, clinical assessments (Fugl-Meyer scale, ARAT), and demographic information – are openly available on the Figshare platform, enabling researchers worldwide to analyze the data without the need for independent data collection.
Implications for Personalized Rehabilitation
“Our dataset has practical value in several key areas. For example, it enables analysis of how brain activity changes as a patient learns to move their hand again. The paper presents a case where, during movement of the paralyzed hand, the damaged hemisphere activated first, followed seconds later by the healthy hemisphere. Understanding such patterns will help clinicians predict how effectively rehabilitation will progress for a particular patient and adjust treatment programs accordingly,” added co-author Lev Yakovlev, a senior research scientist at the Neuro Center of Skoltech.
The combined data from EEG and fNIRS too provides insights into how the healthy hemisphere reorganizes to support the damaged one, and how this process relates to improvements in sensorimotor skills. This knowledge is essential for tailoring therapy and preventing the reinforcement of incorrect compensatory movements.
Citation: Medvedeva, A., et al. (2026). Multisession fNIRS-EEG data of Post-Stroke Motor Recovery. Recordings During Intact and Paretic Hand Movements. Scientific Data. DOI: 10.1038/s41597-026-06803-5