A Novel Human-Machine Dual-Task Gaming Framework for Visual-Attention Training
Fengjun Mu, Jingting Zhang, Zonghai Huang, Chen Chen, Chaobin Zou, Guangkui Song, Hong Cheng
Abstract
Efficient brain functional training with rehabilitation robots has been an important and challenging topic in the human-machine interaction (HMI) field. Adjusting the interaction and gaming behaviors between human and machine to effectively activate the brain’s functional behavior is still a substantial challenge. In this paper, we take the visual-attention training as an example, and propose a novel human-machine co-gaming interaction framework by integrating a dual-task gaming paradigm and a human–machine gaming strategy. It has a remarkable capability of effectively utilizing the gaming characteristics of HMI behaviors and tasks, to effectively and precisely activate the human’s active attention and passive attention for training. Specifically, we design a gaze-driven dual-task gaming paradigm to co-activate the active and passive attention-network competition for systematically engaging human visual-attention allocation and training. We further develop a reinforcement-learning-based human–machine gaming strategy to adjust the task parameters for improving the attention training efficiency. Consequently, we conduct an experiment study with 8 healthy participants, by jointly analyzing participants’ EEG and eye-tracking data through the training process. Results show that our method can achieve improvement of brain engagement by an average of 15.6% over the widely-employed staircase strategy.