EEG signals of KMI levels of the right and left hands

This database contains raw EEG signals recorded during Kinesthetic Motor Imagery (KMI) at 0%, 40%, 70%, and 100% of the Maximal Voluntary Contraction (MVC) for the right and left hands.

1. Subjects
50 healthy right-handed university students aged 17–30 years (23 females, 27 males; mean age = 21.78 ± 2.66 years). Participants were instructed to sleep at least 8 hours the night before the experiment and to avoid consuming caffeine, alcohol, or other psychoactive substances prior to the EEG session. Each subject performed KMI levels with only one hand.
*Subjects S01 to S25 performed the task with the left hand.
*Subjects S26 to S50 performed the task with the right hand.
2. Experimental Design
Each subject performed kinesthetic motor imagery (KMI) tasks involving hand-grip at 0%, 10%, 40%, 70%, and 100% of MVC. The experimental protocol for each condition consisted of 84 seconds of continuous EEG recording, including 10 repetitions of 4 s of task execution followed by 4 s of rest, with initial and final resting period. The resting periods correspond to level 0%.
3. EEG Device
The used EEG headset was the Cognionics Quick-20m with 20 surface EEG electrodes were positioned at locations Fp1, Fp2, F7, F3, Fz, F4, F8, T7, C3, Cz, C4, T8, A2, P7, P3, Pz, P4, P8, O1, and O2.
4. Files
A file named info_subjects.xlsx containing the metadata of all participants is provided. This information was collected through a self-reported questionnaire and standardized psychometric tests applied individually to each subject prior to the EEG recordings.
Data Organization
Folder named LEFT (e.g., S01, S02, …, S25). KMI levels for the left hand.
Folder named RIGHT (e.g., S26, S27, …, S50). KMI levels for the right hand.
Files per subject:
4 Kinesthetic Motor Imagery (KMI) EEG files.
Each .csv file contains 20 columns, corresponding to the 20 EEG electrodes, and rows representing raw time samples.
All EEG signals are provided in a fully raw format, without filtering or pre-processing.
The first row of every .csv file contains the electrode name associated with each column.
This structure enables direct channel identification without requiring separate channel-mapping files.
The dataset is therefore ready for automated processing in Python, MATLAB, and C++, as well as in machine learning pipelines.