Continuously Decoding Grasping Movements using Stereotactic Depth Electrodes

Ottenhoff, Maarten C; Wagner, Louis; Tousseyn, Simon; Colon, Albert; Kubben, Pieter L.

doi:10.1109/embc46164.2021.9629639

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Subsequently, experiment initializes a recorder instance and adds all streams to the list of streams it should record. For a movement experiment [19][20][21][22][23], the streams recorded could be the neural amplifier and experimental triggers. Additionally, a movement tracker [24][25][26] or a force sensor [27] could be added.…”

Section: Setupmentioning

confidence: 99%

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

Amigó-Vega,

Ottenhoff,

Verwoert

et al. 2023

JMIR Neurotech

Self Cite

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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: Setupmentioning

confidence: 99%

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

show abstract

“…Subsequently, initializes a Recorder instance and adds all streams to the list of streams it should record. For a movement experiment ([12], [13], [14], [15], [16]), the streams recorded could be the neural amplifier, experimental triggers, and a movement tracker could be potentially added ([17], [18], [19]) or a force sensor ([20]. For speech perception ([21], [22], [23]) or auditory perception ([24], [25]) the audio stream, experiment triggers and neural data.…”

Section: T-rex Platformmentioning

confidence: 99%

T-Rex: sTandalone Recorder of EXperiments; An easy and versatile neural recording platform

Amigó-Vega

Ottenhoff

Verwoert

et al. 2022

Preprint

Self Cite

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Recording time in invasive neuroscientific empirical research is short and must be used as efficiently as possible. Time is often lost due to long setup times and errors by the researcher. Minimizing the number of manual actions reduces both and can be achieved by automating as much as possible. Importantly, automation should not reduce the flexibility of the system. Currently, recording setups are either custom-made by the researchers or provided as a module in comprehensive neuroscientific toolboxes, and no platforms exist focused explicitly on recording. Therefore, we developed a lightweight, flexible, platform- and measurement-independent recording system that can start and record experiments with a single press of a button. Data synchronization and recording are based on Lab Streaming Layer to ensure that all major programming languages and toolboxes can be used to develop and execute experiments. We have minimized the user restrictions as much as possible and imposed only two requirements on the experiment: The experiment should include a Lab Streaming Layer stream, and it should be able to run from a command line call. Further, we provided an easy-to-use interface that can be adjusted to specific measurement modalities, amplifiers, and participants. The presented system provides a new way of setting up and recording experiments for researchers and participants. Because of the automation and easy-to-use interface, the participant could even start and stop experiments by themselves, thus potentially providing data without the experimenter's presence.

show abstract

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

Amigó-Vega,

Ottenhoff,

Verwoert

et al. 2023

Preprint

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

Continuously Decoding Grasping Movements using Stereotactic Depth Electrodes

Cited by 3 publications

References 22 publications

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

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

T-Rex: sTandalone Recorder of EXperiments; An easy and versatile neural recording platform

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

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