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

Abstract

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.

Comparison of acoustic-to-articulatory and brain-to-articulatory mapping during speech production using ultrasound tongue imaging and EEG

“…In Sec. 1, we have shown several previous approaches for brainto-speech synthesis with articulatory information included; but such articulation was always indirectly measured / estimated using acoustic-to-articulatory inversion, and not recorded in parallel with brain-related data [22,27,28,29,30]. As suggested by the papers above, an obvious solution for speech BCIs is the examination of articulation as an intermediate representation between the brain signal and the resulting final speech, which we dealt with in [26] and extended in this article with ultrasound-based acoustic-to-articulatory inversion.…”

“…Several studies appeared this year which have the aim to predict articulatory-related information from the brain signal. Amigó-Vega et al [29] and Wairagkar et al [30] both use invasive EEG for brain representation. The former has VocalTractLab parameters as the target [29], whereas the latter aims to predict EMA representation resulting from speakerindependent AAI with pre-trained models [30].…”

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

“…We see no indications of different data quality in our neural decoding endeavors. We can decode speech [44,45] and movement trajectories [46] with performance equal to that using our previous setup.…”