IEEE J Biomed Health Inform. 2024 Jul;28(7):3965-3972. doi: 10.1109/JBHI.2024.3395548. Epub 2024 Jul 2.
Stress is revealed by the inability of individuals to cope with their environment, which is frequently evidenced by a failure to achieve their full potential in tasks or goals. This study aims to assess the feasibility of estimating the level of stress that the user is perceiving related to a specific task through an electroencephalograpic (EEG) system. This system is integrated with a Serious Game consisting of a multi-level stress driving tool, and Deep Learning (DL) neural networks are used for classification. The game involves controlling a vehicle to dodge obstacles, with the number of obstacles increasing based on complexity. Assuming that there is a direct correlation between the difficulty level of the game and the stress level of the user, a recurrent neural network (RNN) with a structure based on gated recurrent units (GRU) was used to classify the different levels of stress. The results show that the RNN model is able to predict stress levels above current state-of-the-art with up to 94% accuracy in some cases, suggesting that the use of EEG systems in combination with Serious Games and DL represents a promising technique in the prediction and classification of mental stress levels.
压力表现为个体无法应对其环境，这通常表现为未能充分发挥其在任务或目标中的潜力。本研究旨在评估通过脑电图（EEG）系统来估计用户感知与特定任务相关的压力水平的可行性。该系统与一个包含多级别压力驱动工具的严肃游戏集成，并且使用深度学习（DL）神经网络进行分类。游戏涉及控制车辆躲避障碍物，障碍物的数量根据复杂度而增加。假设游戏的难度级别与用户的压力水平之间存在直接关联，因此使用基于门控循环单元（GRU）的结构的递归神经网络（RNN）来对不同的压力级别进行分类。结果表明，RNN 模型能够预测压力水平，在某些情况下准确率高达 94%，超过了当前的最先进水平，这表明 EEG 系统与严肃游戏和 DL 的结合在精神压力水平的预测和分类方面是一种很有前途的技术。
