Sciaraffa Nicolina, Di Flumeri Gianluca, Germano Daniele, Giorgi Andrea, Di Florio Antonio, Borghini Gianluca, Vozzi Alessia, Ronca Vincenzo, Babiloni Fabio, Aricò Pietro
BrainSigns Srl, Rome, Italy.
Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.
Front Hum Neurosci. 2022 Jul 14;16:901387. doi: 10.3389/fnhum.2022.901387. eCollection 2022.
Technologies like passive brain-computer interfaces (BCI) can enhance human-machine interaction. Anyhow, there are still shortcomings in terms of easiness of use, reliability, and generalizability that prevent passive-BCI from entering real-life situations. The current work aimed to technologically and methodologically design a new gel-free passive-BCI system for out-of-the-lab employment. The choice of the water-based electrodes and the design of a new lightweight headset met the need for easy-to-wear, comfortable, and highly acceptable technology. The proposed system showed high reliability in both laboratory and realistic settings, performing not significantly different from the gold standard based on gel electrodes. In both cases, the proposed system allowed effective discrimination (AUC > 0.9) between low and high levels of workload, vigilance, and stress even for high temporal resolution (<10 s). Finally, the generalizability of the proposed system has been tested through a cross-task calibration. The system calibrated with the data recorded during the laboratory tasks was able to discriminate the targeted human factors during the realistic task reaching AUC values higher than 0.8 at 40 s of temporal resolution in case of vigilance and workload, and 20 s of temporal resolution for the stress monitoring. These results pave the way for ecologic use of the system, where calibration data of the realistic task are difficult to obtain.
像被动式脑机接口(BCI)这样的技术可以增强人机交互。然而,在易用性、可靠性和通用性方面仍存在不足,这阻碍了被动式BCI进入实际生活场景。当前的工作旨在从技术和方法上设计一种新的无凝胶被动式BCI系统,用于实验室外的应用。水基电极的选择和新型轻便头戴设备的设计满足了易于佩戴、舒适且高度可接受的技术需求。所提出的系统在实验室和实际环境中均显示出高可靠性,其性能与基于凝胶电极的金标准相比无显著差异。在这两种情况下,所提出的系统即使对于高时间分辨率(<10秒),也能有效区分低水平和高水平的工作量、警觉性和压力。最后,通过跨任务校准测试了所提出系统的通用性。用实验室任务期间记录的数据进行校准的系统能够在实际任务中区分目标人为因素,在警觉性和工作量方面,时间分辨率为40秒时,曲线下面积(AUC)值高于0.8,在压力监测方面,时间分辨率为20秒时,AUC值高于0.8。这些结果为该系统的生态应用铺平了道路,在实际任务中校准数据难以获取的情况下尤其如此。