Biology subjects

Jongejan, S. L.

Publications and source records attributed to Jongejan, S. L..

2 recordsLinked to original sources

bioRxiv2025

Deepening sleep using an EEG wearable featuring modeling-based closed-loop neurostimulation

ObjectiveClosed-loop neurostimulation (CLNS) during slow-wave sleep (SWS) has been shown to enhance slow-wave activity, predominantly using laboratory equipment. To further advance CLNS research and its potential applications, there is a need for user-friendly EEG wearable equipment featuring CLNS that can support long-term CLNS studies in both clinical and home settings. ApproachHere we evaluate whether modeling-based CLNS (M-CLNS) with acoustic stimulation of slow oscillations (SOs) can be effectively implemented using a self-applicable EEG headband with forehead electrodes. We assess the performance of M-CLNS stimulus targeting over the EEG headband together with short-term, stimulus-locked, and more enduring enhancement of SWS. We validate our results against simultaneous recordings obtained using gold-standard laboratory PSG equipment. Main resultsOur findings demonstrate that the SO phase can be reliably assessed and accurately targeted using M-CLNS with the EEG headband. We show an immediate enhancement of SO dynamics in the short-term, as well as an enduring increase in power spectral density across SO and delta frequencies (0.75 - 5 Hz) across SWS. These results are in line with previous studies using M-CLNS with laboratory equipment. SignificanceThese findings demonstrate that M-CLNS with acoustic stimulation can successfully be applied using an EEG headband on the forehead to target SOs, leading to both immediate and enduring enhancements of SWS. In conclusion, M-CLNS in a self-applicable EEG headband may offer a promising tool for portable and non-invasive enhancement of SWS, with future potential for clinical and home-based applications.

neuroscience↗

bioRxiv2024

Predicting and phase targeting brain oscillations in real-time

ObjectiveClosed-loop neurostimulation (CLNS) procedures, aligning stimuli with electrical brain activity, are quickly gaining popularity in neuroscience. They have been employed to reveal causal links between neural activity patterns and function, and to explore therapeutic effects of electroencephalography (EEG-)guided stimulations during sleep. Most CLNS procedures are developed for a single purpose, detecting one specific pattern of interest in the EEG. Furthermore, most procedures have limited, if any, flexibility to adapt to temporal or interindividual variance in the signal, which means they wouldnt work optimally across the full physiological phenomenology. ApproachHere we present a new approach to CLNS, based on real-time signal modelling to predict brain activity, allowing targeting of a broad variety of oscillatory dynamics. Intrinsic to the modelling approach is adaptation to signal variance, such that no personalization steps prior to use are necessary. We systematically assess stimulus targeting performance of modelling-based CLNS (M-CLNS), across a wide range of brain oscillation frequencies and phases in human and rodent neurophysiological signals. Main resultsOur results show high performance for all target phases and frequency bands, including slow oscillations, theta and alpha waves. SignificanceThese findings highlight the general applicability and adaptability of M-CLNS, which also favors its application in populations with an atypical oscillatory signature, like clinical or elderly populations. In conclusion, M-CLNS provides a promising new tool for neural activity-dependent stimulation in both experimental research and therapeutic applications, such as enhancing deep sleep in patients with sleep disorders.

neuroscience↗