EEG Recordings of Mental Commands

Published: 4 December 2023| Version 2 | DOI: 10.17632/zd2z3gg228.2
mohammad lataifeh,
, Roudha Ali


Research Hypothesis: The research hypothesis posits that discernible patterns exist in the EEG (Electroencephalogram) recordings of individuals aged between 18 and 25 while performing mental commands related to lifting, dropping, pulling, pushing, and moving right. The study suggests that distinct neural signatures corresponding to each mental command can be observed in the EEG data. Data Collection: The dataset comprises 30 raw EEG recordings obtained from participants aged 18 to 25, ensuring they had no prior head injuries or illnesses. Participants were presented with a visual cue in the form of a box, and they were instructed to imagine performing specific mental commands. The experimental protocol involved a series of mental commands, each lasting for 3 minutes, separated by 10-second breaks. The sequence of mental commands included lifting, dropping, pulling, pushing, and moving right. Procedure: 1. Lift Command (3 minutes): Participants were shown a box and instructed to mentally focus on the concept of lifting for 3 minutes, followed by a 10-second break. 2. Drop Command (3 minutes): Similar to the lift command, participants mentally concentrated on the act of dropping for 3 minutes, again followed by a 10-second break. 3. Pull Command (3 minutes): Participants mentally engaged in the concept of pulling for 3 minutes, with a subsequent 10-second break. 4. Push Command (3 minutes): The mental task shifted to pushing for another 3 minutes, followed by a 10-second break. 5. Move Right Command (3 minutes): Participants were instructed to mentally command a movement to the right for 3 minutes, concluding with a 10-second break. Interpretation of Data: The analysis of the raw EEG data aims to identify specific neural patterns associated with each mental command. Given the presentation of a visual cue (the box), changes in electrical brain activity during different mental tasks are anticipated. Analysis methods may include time-frequency decomposition, event-related potentials (ERPs), or machine learning techniques to reveal patterns unique to each command. Future Directions: The dataset opens avenues for further investigations, such as exploring the development of brain-computer interfaces (BCIs) for command recognition based on EEG signals. Additionally, correlations between individual differences (e.g., cognitive abilities) and EEG patterns could be explored to enhance our understanding of the neural correlates of mental commands in this specific age group. In conclusion, this dataset, which includes the visual cue of a box, offers valuable insights into the neural dynamics associated with mental commands. It has the potential to advance our understanding of cognitive processes and contribute to the development of applications in brain-computer interface technology.



University of Sharjah


Brain-Computer Interface