Closed-Loop Mu-Rhythm Brain–Computer Interface for Neuroadaptive Control of the Chrome Dinosaur Game

Abstract

Background: Mu-rhythm BCIs provide a noninvasive entry to motor control via event-related desynchronization detection over the sensorimotor cortex during motor imagery. Standard training paradigms are immersive and result in slow learning. The current study combines gamification and closed-loop vibrotactile neurofeedback with the Chrome Dinosaur game for increased BCI performance and user enjoyment.

Objective: To determine if a low-cost, mu-rhythm BCI with tactile neurofeedback can enhance motor imagery control accuracy and induce neuroplastic changes in healthy users within a gamified setting.

Methods: Twenty participants used right-hand motor imagery to generate "jump" and "duck" movements in the Chrome Dinosaur game using mu-power desynchronization, which was recorded at C3, Cz, and C4 electrodes. Real-time EEG was labeled using an Arduino microcontroller that gave vibrotactile feedback upon correct classification. Participants had 10 runs (300 trials total) with pre- and post-session resting-state EEG recordings.

Results: BCI training improved Dino game control via motor imagery, with jump and duck accuracies rising to 78.5% and 75.1% by Run 10. Reaction times dropped from 920 ms to 640 ms. Significant gains were confirmed by ANOVA (p < 0.001). Mu-power decreased and modulation depth increased, indicating enhanced sensorimotor activation. Keyboard scores remained stable, suggesting BCI-specific learning.

Conclusion: Gamified, closed-loop mu-rhythm BCIs with tactile feedback can facilitate rapid learning and modulate cortical oscillations, providing an appealing model for large-scale, user-driven neurorehabilitation devices.