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

该研究计划源于Tech 3Lab的各种研究项目，Tech 3Lab是一个专门使用多方法方法测量用户体验的研究实验室。在Tech 3lab，我们联合使用眼动仪，脑电图和收集许多生理测量（呼吸，心率，皮肤电反应和面部情绪）的设备，同时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）。这种联合分析这些类型的复杂数据的新可能性需要开发新的统计模型和更有效的统计技术，以更好地回答我们领域特有的研究问题。例如，在事件相关电位（ERP）或眼注视相关电位（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，这个问题通常是通过收集大量ERP/EFRP试验的数据来解决的，以便平均噪音。然而，这往往是不可能的研究问题，我们要保持现实的实验。因此，需要开发有效的统计方法，明确测试EEG数据与有限数量的ERP/EFRP试验是明确的。另一种情况下，更有效的统计技术将是有用的是在联合建模的EEG数据同时收集不同的主题。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现象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.