EEG-Based Dataset Explicitly Targets the Transitions between Sitting and Standing for Exploring Neural Activation Patterns in Motor Imagery and Execution

This study presents the first publicly accessible electroencephalography (EEG) dataset explicitly targeting sit-to-stand and stand-to-sit transitions during both motor execution (ME) and motor imagery (MI) tasks. Twenty-two healthy participants performed sitting and standing transitions under well-controlled experimental conditions while 60-channel EEG, electrooculography (EOG), and electromyography (EMG) signals were synchronously recorded. The dataset enables the exploration of neural activation patterns associated with lower-limb movements and supports the development of EEG-based brain–computer interface (BCI) algorithms for mobility assistance and rehabilitation. To validate the dataset, a benchmark classification was conducted using EEGNet, a compact convolutional neural network. Results demonstrated consistent decoding performance with mean accuracies of approximately 80% for ME and 70% for MI, indicating the reliability and usability of the dataset. Additionally, analyses of movement-related cortical potentials (MRCPs) and event-related desynchronization/synchronization (ERD/ERS) patterns revealed distinct neural signatures across the transition phases. This dataset provides a comprehensive foundation for studying lower-limb motor control, neural dynamics, and the advancement of MI-based BCIs for rehabilitation and assistive technologies.

The raw and preprocessed data are available via the following URLs in the open-access online repository, Zenodo (https://zenodo.org).

• raw data: https://zenodo.org/records/17561969

• preprocessed data: https://zenodo.org/records/17629950