Building a digital twin framework for EEG neurofeedback in cricket batting motor imagery using simulated data

Pathak, Devanka; Herrema, Ron; Heffer, Naomi; Yang, Hongji

Abstract

This paper presents a digital twin framework designed to provide neurofeedback for a cricket batting motor imagery (MI) study. The framework uses simulated EEG data to establish a computational model that can be extended to real-time data integration. Participants imagining cricket batting strokes from video cues will be connected to an EEG Emotiv Pro headset, and the collected data will be used to provide neurofeedback through the proposed digital twin model. The progress made in building the pipeline is documented, highlighting steps that work without error and suggesting further developments needed to complete the digital twin model for the scheduled MI experiment.

• Presentation Conference Type: Presentation / Talk
• Conference Name: 2024 IEEE International Conference on Digital Twin
• Start Date: Dec 2, 2024
• Acceptance Date: Sep 15, 2024
• Deposit Date: Jan 16, 2025
• Peer Reviewed: Peer Reviewed
• Public URL: https://uwe-repository.worktribe.com/output/13608683