Eggly: Designing Mobile Augmented Reality Neurofeedback Training Games for Children with Autism Spectrum Disorder

TL;DR

Eggly presents a mobile augmented reality neurofeedback training game for children with Autism Spectrum Disorder, built on a consumer EEG headband and tablet to enable scalable NFT in nonclinical settings. Through a ten-month co-design with domain experts, the authors establish a multilevel, multimodal feedback framework and adaptive thresholds, and implement AR-enhanced game elements, customization, and WOZ-based 3D character integration. Field studies (n=5) and a deployment study (n=1) show promising feasibility, engagement, and preliminary improvements in social attention, with rich qualitative insights into design choices, AR benefits, and personalization needs. The work demonstrates a practical, accessible path to integrating AR and NFT for ASD interventions and provides a design blueprint for future mobile NFT games.

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects how children communicate and relate to other people and the world around them. Emerging studies have shown that neurofeedback training (NFT) games are an effective and playful intervention to enhance social and attentional capabilities for autistic children. However, NFT is primarily available in a clinical setting that is hard to scale. Also, the intervention demands deliberately-designed gamified feedback with fun and enjoyment, where little knowledge has been acquired in the HCI community. Through a ten-month iterative design process with four domain experts, we developed Eggly, a mobile NFT game based on a consumer-grade EEG headband and a tablet. Eggly uses novel augmented reality (AR) techniques to offer engagement and personalization, enhancing their training experience. We conducted two field studies (a single-session study and a three-week multi-session study) with a total of five autistic children to assess Eggly in practice at a special education center. Both quantitative and qualitative results indicate the effectiveness of the approach as well as contribute to the design knowledge of creating mobile AR NFT games.

Figures (9)

• Figure 1: (1) Customization Phase: A picture of a colored sheet is taken to customize the character in the game.. (2) Calibration Phase: An introductory video explains the game story and tasks while determining the customized thresholds. (3) Training Phase: playing Eggly in AR to collect eggs and control the bird's flight height using the thresholds, with performance being logged. (4) Conclusion Phase: Upon completion of the game, a victory scene is displayed, reporting the overall performance.
• Figure 2: Different animations/movements of characters. The boy (left to right): base position, hands up, head up, catching eggs, turning with eggs, handing over eggs, and turning back. The girl (left to right): base position, turning to the boy, receiving eggs, turning with eggs, putting down eggs, storing eggs, and turning back.
• Figure 3: Left) Different facial expressions on characters (left to right): neutral, happy, and expecting faces for the boy; neutral, smiling, happy, and extremely happy faces for the girl. Right) Examples of designed visual effects in Eggly: (a) 3D emoji, (b) golden egg, (c) heart-shaped bubbles, (d) shining stars, and (e) bubbles.
• Figure 4: Randomly-colorized eggs laid by the bird to enhance game engagement.
• Figure 5: (a) Study setup at the special education center. (b) A child was coloring the characters during the field study and (c) his finished coloring sheet. (d-f) Gameplay sessions with caregivers/guardians during the studies.