Jacobsen Shoham, Meiron Oded, Salomon David Yoel, Kraizler Nir, Factor Hagai, Jaul Efraim, Tsur Elishai Ezra
1Department of Computer ScienceJerusalem College of TechnologyJerusalem91160Israel.
2Clinical Research Center for Brain SciencesHerzog Medical CenterJerusalem91120Israel.
IEEE J Transl Eng Health Med. 2020 May 6;8:2200208. doi: 10.1109/JTEHM.2020.2989768. eCollection 2020.
EEG-driven research is paramount in cognitive-neuropsychological studies, as it provides a non-invasive window to the underlying neural mechanisms of cognition and behavior. A myriad collection of software and hardware frameworks has been developed to alleviate some of the technical barriers involved in EEG-driven research. we propose an integrated development environment which encompasses the entire technical "data-collection pipeline" of cognitive-neuropsychological research, including experiment design, data acquisition, data exploration and analysis in a state-of-the-art user interface. Our framework is based on a unique integration between a python-based web framework, time-oriented databases and object-based data schemes. we demonstrated our framework with the recording and analysis of an n-Back task completed by 15 elderly (ages 50 to 80) participants. This case study demonstrates the highly utilized nature of our integrated framework with a challenging target population. Furthermore, our results may provide new insights into the correlation between brain activity and working memory performance in elderly people, who are prone to experience accelerated decline in executive prefrontal cortex functioning. our framework extends the range of EEG-driven experimental methods for assessing cognition available for cognitive-neuroscientists, allowing them to concentrate on the creative part of their work instead of technical aspects.
脑电图驱动的研究在认知神经心理学研究中至关重要,因为它为认知和行为的潜在神经机制提供了一个非侵入性的窗口。已经开发了大量的软件和硬件框架来缓解脑电图驱动研究中涉及的一些技术障碍。我们提出了一个集成开发环境,它涵盖了认知神经心理学研究的整个技术“数据收集管道”,包括实验设计、数据采集、在一个先进的用户界面中进行数据探索和分析。我们的框架基于基于Python的Web框架、面向时间的数据库和基于对象的数据方案之间的独特集成。我们通过对15名年龄在50至80岁之间的老年参与者完成的n-back任务进行记录和分析,展示了我们的框架。这个案例研究证明了我们的集成框架在具有挑战性的目标人群中的高利用率。此外,我们的结果可能为老年人的大脑活动与工作记忆表现之间的相关性提供新的见解,老年人容易出现前额叶执行功能加速衰退的情况。我们的框架扩展了认知神经科学家可用于评估认知的脑电图驱动实验方法的范围,使他们能够专注于工作的创造性部分而非技术方面。