Auditory Cognition and Psychoacoustics Team, Lyon Neuroscience Research Center, CNRS-UMR 5292, Institut National de la Santé et de la Recherche Médicale U1028, Université Claude Bernard Lyon 1, 69675 Lyon, France
eNeuro. 2021 Aug 25;8(4). doi: 10.1523/ENEURO.0524-20.2021. Print 2021 Jul-Aug.
In auditory behavioral and EEG experiments, the variability of stimulation solutions, for both software and hardware, adds unnecessary technical constraints. Currently, there is no easy to use, inexpensive, and shareable solution that could improve collaborations and data comparisons across different sites and contexts. This article outlines a system composed by a Raspberry Pi coupled with Python programming and associated with a HifiBerry sound card. We compare its sound performances with those of a wide variety of materials and configurations. This solution achieves the high timing accuracy and sound quality important in auditory cognition experiments, while being simple to use and open source. The present system shows high performances and results along with excellent feedback from users. It is inexpensive, easy to build, share, and improve on. Working with such low-cost, powerful, and collaborative hardware and software tools allows people to create their own specific, adapted, and shareable system that can be standardized across different collaborative sites, while being extremely simple and robust in use.
在听觉行为和 EEG 实验中，刺激溶液的可变性（无论是软件还是硬件）都会增加不必要的技术限制。目前，还没有一种易于使用、价格低廉且可共享的解决方案，可以改善不同地点和环境下的协作和数据比较。本文概述了一个由 Raspberry Pi 与 Python 编程结合，并与 HifiBerry 声卡相关联的系统。我们将其声音性能与各种材料和配置进行了比较。该解决方案实现了听觉认知实验中重要的高精度定时和高质量声音，同时使用简单且开源。目前的系统具有出色的性能和结果，并且得到了用户的高度评价。它价格低廉、易于构建、分享和改进。使用这种低成本、功能强大且协作性强的硬件和软件工具，人们可以创建自己的特定、适应和可共享的系统，该系统可以在不同的协作站点之间进行标准化，同时在使用上非常简单和稳健。
