Labonte-Lemoyne Elise, Courtemanche François, Louis Victoire, Fredette Marc, Sénécal Sylvain, Léger Pierre-Majorique
Tech3Lab, HEC Montréal, Université de Montréal, Montreal, QC, Canada.
Front Hum Neurosci. 2018 Jul 17;12:282. doi: 10.3389/fnhum.2018.00282. eCollection 2018.
Passive Brain-Computer interfaces (pBCIs) are a human-computer communication tool where the computer can detect from neurophysiological signals the current mental or emotional state of the user. The system can then adjust itself to guide the user toward a desired state. One challenge facing developers of pBCIs is that the system's parameters are generally set at the onset of the interaction and remain stable throughout, not adapting to potential changes over time such as fatigue. The goal of this paper is to investigate the improvement of pBCIs with settings adjusted according to the information provided by a second neurophysiological signal. With the use of a second signal, making the system a hybrid pBCI, those parameters can be continuously adjusted with dynamic thresholding to respond to variations such as fatigue or learning. In this experiment, we hypothesize that the adaptive system with dynamic thresholding will improve perceived game experience and objective game performance compared to two other conditions: an adaptive system with single primary signal biocybernetic loop and a control non-adaptive game. A within-subject experiment was conducted with 16 participants using three versions of the game Tetris. Each participant plays 15 min of Tetris under three experimental conditions. The control condition is the traditional game of Tetris with a progressive increase in speed. The second condition is a cognitive load only biocybernetic loop with the parameters presented in Ewing et al. (2016). The third condition is our proposed biocybernetic loop using dynamic threshold selection. Electroencephalography was used as the primary signal and automatic facial expression analysis as the secondary signal. Our results show that, contrary to our expectations, the adaptive systems did not improve the participants' experience as participants had more negative affect from the BCI conditions than in the control condition. We endeavored to develop a system that improved upon the authentic version of the Tetris game, however, our proposed adaptive system neither improved players' perceived experience, nor their objective performance. Nevertheless, this experience can inform developers of hybrid passive BCIs on a novel way to employ various neurophysiological features simultaneously.
被动式脑机接口（pBCIs）是一种人机通信工具，计算机可以从神经生理信号中检测用户当前的心理或情绪状态。然后，系统可以自行调整，引导用户达到期望状态。pBCIs开发者面临的一个挑战是，系统参数通常在交互开始时设置，并且在整个过程中保持稳定，无法适应随时间推移而产生的潜在变化，如疲劳。本文的目的是研究根据第二个神经生理信号提供的信息调整设置，以改进pBCIs。通过使用第二个信号，使系统成为混合pBCI，这些参数可以通过动态阈值进行连续调整，以应对疲劳或学习等变化。在本实验中，我们假设与其他两种情况相比，具有动态阈值的自适应系统将改善感知游戏体验和客观游戏性能：一种是具有单一主要信号生物控制论回路的自适应系统，另一种是对照非自适应游戏。对16名参与者进行了一项采用三种版本俄罗斯方块游戏的受试者内实验。每位参与者在三种实验条件下玩15分钟的俄罗斯方块。对照条件是传统的俄罗斯方块游戏，速度逐渐增加。第二种条件是仅具有认知负荷的生物控制论回路，其参数如尤因等人（2016年）所述。第三种条件是我们提出的使用动态阈值选择的生物控制论回路。脑电图被用作主要信号，自动面部表情分析被用作次要信号。我们的结果表明，与我们的预期相反，自适应系统并没有改善参与者的体验，因为与对照条件相比，参与者在BCI条件下有更多的负面影响。我们努力开发一种比俄罗斯方块游戏原版有所改进的系统，然而，我们提出的自适应系统既没有改善玩家的感知体验，也没有提高他们的客观表现。尽管如此，这段经历可以为混合被动式脑机接口的开发者提供一种新颖的方式，即同时利用各种神经生理特征。
