Date of Award
5-2024
Culminating Project Type
Starred Paper
Styleguide
ieee
Degree Name
Computer Science: M.S.
Department
Computer Science and Information Technology
College
School of Science and Engineering
First Advisor
Dr. Adriano Cavalcanti
Second Advisor
Dr. Akalanka Bandara Mailewa
Third Advisor
Dr. Andrew A. Anda
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Keywords and Subject Headings
Brain-Computer Interface (BCI), Electroencephalography (EEG), TensorFlow, Convolutional Neural Network (CNN)
Abstract
Developed a 1-Dimensional Convolutional Neural Network model and trained to translate electroencephalography (EEG) signals into drone commands. Using a 16-channel EEG headset at a 125 Hz sampling rate raw data was collected and further processed for feature extraction and training of the model to control drone movements using commands, backward, forward, left, right, land, and takeoff based on the classification of brain wave patterns. The developed model was trained on raw EEG dataset of 2,498,750 entries and achieved an accuracy of 99.27%. Though the model performed well in classifying the brain wave patterns, based on commands, it struggled slightly in differentiating 'Takeoff' and 'Forward' commands because of the non-uniform size of the dataset.
Recommended Citation
Suresh Babu, Akshay, "Optimizing BCI for Real-Time Performance with TensorFlow" (2024). Culminating Projects in Computer Science and Information Technology. 53.
https://repository.stcloudstate.edu/csit_etds/53
