A Wearable EEG System for Closed-Loop Neuromodulation of High-Frequency Sleep-Related Oscillations
In healthy sleepers, cortical alpha oscillations are present during the transition from wakefulness to sleep, and dissipate at sleep onset. For individuals with insomnia, alpha power is elevated during the wake-sleep transition and can persist throughout the night. Neuromodulation techniques using phase-locked stimulation have been put forth as alternatives to drugs for improving slow-wave sleep quality. Due to technical limitations, this approach has not been tested on faster frequency alpha oscillations. Here we examine the feasibility of using an endpoint-corrected version of the Hilbert Transform (ecHT) algorithm implemented on-device to measure alpha phase and deliver phase-locked auditory stimulation to modulate alpha and promote sleep initiation. First, the ecHT algorithm is implemented on a tabletop electroencephalogram (EEG) device and used to measure the timing of the auditory evoked response and its delivery at precise phases of the alpha oscillation. Secondly, a pilot at-home study tests feasibility to use a wearable version of the neuromodulation device for real-time phase-locked stimulation in the alpha (8-12 Hz) frequency range. Auditory stimulation was delivered at the intended phases of alpha with high precision, and alpha oscillations were affected differently by stimuli delivered at opposing phases. Our wearable system was capable of measuring sleep micro- and macro-events present in the EEG that were appropriate for clinical sleep scoring. Sleep onset latencies were reduced for a subset of subjects displaying sleep onset insomnia symptoms in the stimulation condition. This study demonstrates the feasibility of closed-loop tracking and neuromodulation of alpha oscillations using a wearable EEG device. Preliminary results suggest that this approach could be used to accelerate sleep initiation in individuals with objective insomnia symptoms.
Code (0)
등록된 구현이 없습니다.
Tasks
EEGElectroencephalogram (EEG)Sleep QualitySimilar Papers 제목 키워드 기반
When neural implant meets multimodal LLM: A dual-loop system for neuromodulation and naturalistic neuralbehavioral research
We propose a novel dual-loop system that synergistically combines responsive neurostimulation (RNS) implants with artificial intelligence-driven wearable devices for treating post-traumatic stress disorder (PTSD) and ena…
EEGLarge Language ModelMultimodal Large Language ModelClosed-Loop Neural Prostheses with On-Chip Intelligence: A Review and A Low-Latency Machine Learning Model for Brain State Detection
The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to r…
WAND: A 128-channel, closed-loop, wireless artifact-free neuromodulation device
Closed-loop neuromodulation systems aim to treat a variety of neurological conditions by dynamically delivering and adjusting therapeutic electrical stimulation in response to a patient's neural state, recorded in real-t…
Modern Views of Machine Learning for Precision Psychiatry
In light of the NIMH's Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of menta…
BIG-bench Machine LearningExplainable Artificial Intelligence (XAI)PrognosisOnline Learning Koopman operator for closed-loop electrical neurostimulation in epilepsy
Electrical neuromodulation as a palliative treatment has been increasingly used in the control of epilepsy. However, current neuromodulations commonly implement predetermined actuation strategies and lack the capability …
Computational EfficiencyEEGElectroencephalogram (EEG)Model Predictive Control+1