Page 172 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

REQ4: It is critical that the system facilitate a structured training process, collecting labeled
EEG data from individual users across various mental states (calm, jaw clench, imagined limb
movement) to robustly train and calibrate the machine learning model for personalized control.

REQ5: It is critical that the system efficiently collect, accurately label, and securely store large
volumes of EEG data (e.g., 1.8 million data points per user) locally in a structured format, such
as comma-separated value (CSV) files, for model training and future analysis.

REQ6: It is critical that the system safety features, including an accessible emergency stop
mechanism and real-time monitoring for potential malfunctions or unsafe conditions, to ensure
the user's well-being at all times.

4 Sequence Diagram

Figure 1: Sequence Diagram of our system

5 References

[1.] Hajare, R., & Kadam, S. (2021). Comparative Study Analysis of Practical EEG Sensors in
Medical Diagnoses. Global Transitions Proceedings, 2, 467–472
[2.] Zendehdel, N., Chen, H., Song, Y. S., & Leu, M. C. (2024). Implementing Eye Movement
Tracking for UAV Navigation. Proceedings of the 2024 International Symposium on
Flexible Automation, 2, V001T08A004.
[3.] Basharpoor, S., Heidari, F., & Molavi, P. (2019). EEG coherence in theta, alpha, and beta
bands in frontal regions and executive functions. Applied Neuropsychology: Adult.
[4.] Dev, A., Rahman, M. A., & Mamun, N. (2018). Design of an EEG-based Brain Controlled
Wheelchair for Quadriplegic Patients. 2018 3rd International Conference for Convergence
in Technology (I2CT), 1–5
[5.] Chaddad A, Wu Y, Kateb R, Bouridane A. Electroencephalography Signal Processing:
A Comprehensive Review and Analysis of Methods and Techniques. Sensors (Basel).
2023;23(14):6434. Published 2023 Jul 16. doi:10.3390/s23146434
[6.] I. Iturrate, J. Antelis and J. Minguez, "Synchronous EEG brain-actuated wheelchair with
automated navigation," 2009 IEEE International Conference on Robotics and Automation,
Kobe, Japan, 2009, pp. 2318-2325
[7.] Kumar Chaudhary, A., Gupta, V., Gaurav, K., Kumar Reddy, T., & Behera, L. (2023). EEG
Control of a Robotic Wheelchair. IntechOpen. doi: 10.5772/intechopen.110679

136