Biology subjects

Kubben, P.

Publications and source records attributed to Kubben, P..

3 recordsLinked to original sources

bioRxiv2024

Whole-brain dynamics of articulatory, acoustic and semantic speech representations

Speech production is a complex process that traverses several representations, from the meaning of spoken words (semantic), through the movement of articulatory muscles (articulatory) and, finally, to the produced audio waveform (acoustic). In our study, we aimed to identify how these different representations of speech are spatially and temporally distributed throughout the depth of the brain. By considering multiple representations from the same exact data, we can limit potential con-founders to better understand the different aspects of speech production and acquire crucial complementary information for speech brain-computer interfaces (BCIs). Intracranial speech production data was collected of 15 participants, recorded from 1647 electrode contacts, while they overtly spoke 100 unique words. The electrodes were distributed across the entire brain, including sulci and subcortical areas. We found a bilateral spatial distribution for all three representations, although there was a stronger tuning in the left hemisphere with a more widespread and temporally dynamic distribution than in the right hemisphere. The articulatory and acoustic representations share a similar spatial distribution surrounding the Sylvian fissure, while the semantic representation appears to be widely distributed across the brain in a mostly distinct network. These results highlight the distributed nature of the speech production process and the potential of non-motor representations for speech BCIs.

neuroscience↗

bioRxiv2022

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

AO_SCPLOWBSTRACTC_SCPLOWRecording 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 experimenters presence.

neuroscience↗

bioRxiv2022

Executed and imagined grasping movements can be decoded from lower dimensional representation of distributed non-motor brain areas.

Using brain activity directly as input for assistive tool control can circumvent muscular dysfunction and increase functional independence for physically impaired people. Most invasive motor decoding studies focus on decoding neural signals from the primary motor cortex, which provides a rich but superficial and spatially local signal. Initial non-primary motor cortex decoding endeavors have used distributed recordings to demonstrate decoding of motor activity by grouping electrodes in mesoscale brain regions. While these studies show that there is relevant and decodable movement related information outside the primary motor cortex, these methods are still exclusionary to other mesoscale areas, and do not capture the full informational content of the motor system. In this work, we recorded intracranial EEG of 8 epilepsy patients, including all electrode contacts except those contacts in or adjacent to the central sulcus. We show that executed and imagined movements can be decoded from non-motor areas; combining all non-motor contacts into a lower dimensional representation provides enough information for a Riemannian decoder to reach an area under the curve of 0.83 {+/-} 0.11. Additionally, by training our decoder on executed and testing on imagined movements, we demonstrate that between these two conditions there exists shared distributed information in the beta frequency range. By combining relevant information from all areas into a lower dimensional representation, the decoder was able to achieve high decoding results without information from the primary motor cortex. This representation makes the decoder more robust to perturbations, signal non-stationarities and neural tissue degradation. Our results indicate to look beyond the motor cortex and open up the way towards more robust and more versatile brain-computer interfaces.

neuroscience↗