Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings

Ottenhoff, Maarten C; Verwoert, Maxime; Wagner, Louis; Dijk, Hans van; Kubben, Pieter

doi:10.1101/2023.07.07.548122

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…This experiment is a simple text-based instruction for a grasping task (Figure 4A). The participant is prompted by text in a Python Tkinter [37] window to continuously open and close either the left or right hand, as used previously [38]. The experiment requires neural data as the input device and generates a StreamOutlet to send markers that inform about the start and end of the experiment and of the trials.…”

Section: Case 1: Simple Experiments In Pythonmentioning

confidence: 99%

The Easy and Versatile Neural Recording Platform (T-REX): Design and Development Study (Preprint)

Amigó-Vega,

Ottenhoff,

Verwoert

et al. 2023

Preprint

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BACKGROUND Recording time in invasive neuroscientific research is limited and must be used as efficiently as possible. Time is often lost due to a long setup time and errors by the researcher, driven by the number of manually performed steps. Currently, recording solutions that automate experimental overhead are either custom-made by researchers or provided as a submodule in comprehensive neuroscientific toolboxes, and there are no platforms 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 T-REX (Standalone Recorder of Experiments) that specifically simplifies the recording of experiments while focusing on retaining flexibility. METHODS The proposed solution is a standalone webpage that the researcher can provide without an active internet connection. It is built using Bootstrap5 for the frontend and the Python package Flask for the backend. Only Python 3.7+ and a few dependencies are required to start the different experiments. Data synchronization is implemented using Lab Streaming Layer, 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 experimental overhead during recordings to a minimum. Multiple experiments are centralized in a simple local web interface that reduces an experiment’s setup, start, and stop to a single button press. In principle, any type of experiment, regardless of the scientific field (eg, behavioral or cognitive sciences, and 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 setup, easy recording, and easy-to-use interface, participants may even start and stop experiments by themselves, thus potentially providing data without the researcher’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 greatly increase the time spent on recording instead of setting up (with its possible errors).

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Section: Case 1: Simple Experiments In Pythonmentioning

confidence: 99%

The Easy and Versatile Neural Recording Platform (T-REX): Design and Development Study (Preprint)

Amigó-Vega,

Ottenhoff,

Verwoert

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

The Easy and Versatile Neural Recording Platform (T-REX): Design and Development Study

Amigó-Vega,

Ottenhoff,

Verwoert

et al. 2023

JMIR Neurotech

Self Cite

View full text Add to dashboard Cite

Background Recording time in invasive neuroscientific research is limited and must be used as efficiently as possible. Time is often lost due to a long setup time and errors by the researcher, driven by the number of manually performed steps. Currently, recording solutions that automate experimental overhead are either custom-made by researchers or provided as a submodule in comprehensive neuroscientific toolboxes, and there are no platforms 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 T-REX (Standalone Recorder of Experiments) that specifically simplifies the recording of experiments while focusing on retaining flexibility. Methods The proposed solution is a standalone webpage that the researcher can provide without an active internet connection. It is built using Bootstrap5 for the frontend and the Python package Flask for the backend. Only Python 3.7+ and a few dependencies are required to start the different experiments. Data synchronization is implemented using Lab Streaming Layer, 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 experimental overhead during recordings to a minimum. Multiple experiments are centralized in a simple local web interface that reduces an experiment’s setup, start, and stop to a single button press. In principle, any type of experiment, regardless of the scientific field (eg, behavioral or cognitive sciences, and 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 setup, easy recording, and easy-to-use interface, participants may even start and stop experiments by themselves, thus potentially providing data without the researcher’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 greatly increase the time spent on recording instead of setting up (with its possible errors).

show abstract

Global motor dynamics - Invariant neural representations of motor behavior in distributed brain-wide recordings

Cited by 2 publications

References 39 publications

The Easy and Versatile Neural Recording Platform (T-REX): Design and Development Study (Preprint)

The Easy and Versatile Neural Recording Platform (T-REX): Design and Development Study (Preprint)

The Easy and Versatile Neural Recording Platform (T-REX): Design and Development Study

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