What question did this study set out to answer?

Investigate the relationship between neural and vascular responses during post-stroke motor recovery.

February 16, 2026Open Access

Multisession fNIRS-EEG data of Post-Stroke Motor Recovery. Recordings During Intact and Paretic Hand Movements

AMAlexandra MedvedevaArtificial Intelligence Research Institute NSNikolay SyrovSkolkovo Institute of Science and Technology LYLev YakovlevSkolkovo Institute of Science and Technology

Key Points

Investigate the relationship between neural and vascular responses during post-stroke motor recovery.
Collected fNIRS and EEG data from 16 post-stroke patients during motor tasks.
Recorded data over 84 rehabilitation sessions with intact and paretic hand movements.
Included raw and processed signals as well as clinical scores and demographics.
Dataset includes simultaneous fNIRS and EEG recordings.
Identified hemodynamic changes linked to motor recovery in stroke patients.
Supports research for neurorehabilitation strategies and brain computer interfaces.

Abstract

Accurate diagnosis and monitoring of recovery after stroke are critical for effective motor rehabilitation. As stroke is inherently associated with impaired cerebral blood flow, functional near-infrared spectroscopy (fNIRS) provides a valuable tool for assessing hemodynamic changes in the brain. When combined with electroencephalography (EEG), this multimodal approach can provide complementary insights into neural and vascular responses during recovery. However, longitudinal datasets combining fNIRS and EEG in stroke populations remain limited. The current article presents an open access dataset with simultaneous fNIRS and EEG recordings from 16 post-stroke patients over 84 rehabilitation sessions. Participants performed motor tasks with both paretic and intact hands. The dataset includes raw and processed signals, clinical scores (ARAT, Fugl-Meyer) and patient demographics. This resource supports research into stroke recovery, development of neurorehabilitation strategies and fNIRS-based brain computer interfaces (BCI).

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