Biology subjects

Tousseyn, S.

Publications and source records attributed to Tousseyn, S..

3 recordsLinked to original sources

bioRxiv2023

Decoding Arbitrary and Informed Decisions from Intracranial Recordings in Humans

Ideally, decisions are made based on prior knowledge, which allows for informed choices. Real life, however, often requires us to make decisions arbitrarily, without sufficient information. Decoding decision making processes from neural activity could allow for cognitive neuroprostheses and Brain-Computer Interfaces (BCIs) to support decision processes in rapid human-machine interactions, weigh decision-making confidence, and further enable neuromodulation protocols for the treatment of reward-related dysfunctions. To understand the differences between the decision-making processes in arbitrary and informed decisions, we recorded intracranial electroencephalography in a large number of cortical and subcortical areas from 5 patients during a categorization task. We demonstrate that individual decisions can be decoded from Local Field Potentials (LFPs) before motor response, in both arbitrary and informed conditions. Our analysis revealed dissimilar spatio-temporal patterns between arbitrary and informed decision-making, with arbitrary decisions being decodable in fewer brain regions and earlier in time compared to informed decisions.

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↗

bioRxiv2022

Speech Production in Intracranial Electroencephalography: iBIDS Dataset

Speech production is an intricate process involving a large number of muscles and cognitive processes. The neural processes underlying speech production are not completely understood. As speech is a uniquely human ability, it can not be investigated in animal models. High-fidelity human data can only be obtained in clinical settings and is therefore not easily available to all researchers. Here, we provide a dataset of 10 participants reading out individual words while we measured intracranial EEG from a total of 1103 electrodes. The data, with its high temporal resolution and coverage of a large variety of cortical and sub-cortical brain regions, can help in understanding the speech production process better. Simultaneously, the data can be used to test speech decoding and synthesis approaches from neural data to develop speech Brain-Computer Interfaces and speech neuroprostheses.

neuroscience↗