JMIR Publications

ABSTRACT

Background:

Recording time in invasive neuroscientific research is limited and must be used as efficiently as possible. Time is often lost due to long set-up times and errors by the researcher, driven by the number of manually performed steps. Currently, recording solutions that automate overhead are either custom-made by researchers or provided as a submodule in comprehensive neuroscientific toolboxes, and no platforms exist focused explicitly on recording.

Objective:

Minimizing the number of manual actions may reduce error rates and experimental overhead. However, automation should avoid reducing the flexibility of the system. Therefore, we developed a software package named the sTandalone Recorder of EXperiments (T-Rex), that specifically simplifies the recording of experiments while focusing on retaining flexibility.

Methods:

The proposed solution is a stand-alone webpage that the researcher can serve without an active internet connection. It is built using Bootstrap5 for the front-end and the Python package Flask for the back-end. Only a few Python $3.7+$ dependencies are required to start the different experiments. Data synchronization is implemented using LabStreamingLayer, an open-source networked synchronization ecosystem, enabling all major programming languages and toolboxes to be used for developing and executing the experiments. Additionally, T-Rex runs on Windows, Linux, and macOS.

Results:

The system reduces overhead during recordings to a minimum. Multiple experiments are centralized in a simple local web interface that reduces an experiment's set-up, start and stop into a single button press. In principle, any type of experiment, regardless of scientific field (i.e., behavioral or cognitive sciences, electrophysiology), can be executed with the platform. T-Rex includes an easy-to-use interface that can be adjusted to specific recording modalities, amplifiers, and participants. Because of the automated set-up, recording, and easy-to-use interface, the participant may even start and stop experiments by themselves, thus potentially providing data without the experimenter's presence.

Conclusions:

We developed a new recording platform that is Operating System independent, user-friendly, and robust. We provide researchers with a solution that can significantly increase time spent on recording instead of on the set-up with its possible errors.