Methodological improvement for the statistical analysis of neurophysiological data collected from informations systems

这项研究计划源于Tech3Lab的各种研究项目，Tech3Lab是一个研究实验室，专门使用多种方法来衡量用户体验。在Tech3实验室，我们共同使用眼睛跟踪器、脑电图仪和收集许多生理指标(呼吸、心率、皮肤电反应和面部情绪)的设备，当一个或多个受试者与计算机交互以执行特定任务(访问网站、玩视频游戏、在软件上执行与工作相关的任务等)时。联合分析这些类型的复杂数据的这种新的可能性创造了开发新的统计模型和更有效的统计技术的需要，以适应更好地回答我们领域特定的研究问题。例如，在事件相关电位(ERP)或眼睛注视相关电位(EFRP)实验中，来自脑电数据的噪声信号很难分析。在传统的神经科学研究中，这一问题通常是通过收集大量的ERP/EFRP试验数据来平均噪声来解决的。然而，如果我们想要保持实验的现实性，对于研究问题来说，这通常是不可能的。因此，显然有必要开发有效的统计方法，通过有限数量的ERP/EFRP试验来明确测试EEG数据。另一种更有效的统计技术将有用的情况是对同时收集的不同对象的脑电数据进行联合建模。对大脑之间的连贯性进行更精确的统计分析，将有助于研究多人视频游戏中的心流幽灵或团队合作表现等概念。

This research program stems from the various research projects of the Tech3Lab, a research laboratory who specializes in the use of multi-method approaches for measuring user's experience. At the Tech3lab, we jointly use an eye tracker, electroencephalography and devices collecting many physiological measures (respiration, heart rate, galvanic skin response and facial emotions) while one or many subjects are interacting with a computer to perform a specific task (visiting a website, playing video games, doing work-related tasks on a software, etc.). This new possibility to jointly analyze these types of complex data creates the need to develop new statistical models, and more efficient statistical techniques, adapted to better answer research problems specific to our field. For instance, in event-related potential (ERP) or eye fixation-related potential (EFRP) experiments, the noisy signals coming from electroencephalography data are difficult to analyze. In traditional neuroscience research, this problem is usually solved by collecting data on a very large number of ERP/EFRP trials in order to average out the noise. However, this is often not possible for research problems were we want to preserve the realism of an experiment. The need to develop efficient statistical methods to explicitly test EEG data with a limited number of ERP/EFRP trials is thus clear. Another situation where more efficient statistical techniques will be useful is in the joint modelling of EEG data collected simultaneously on different subjects. A more precise statistical analysis of the coherence between brains will be useful to study concepts like the apparition of flow in multi-player video games or teamwork performance.