Benchmarking real-time algorithms for in-phase auditory stimulation of low amplitude slow waves with wearable EEG devices during sleep
Maria Laura Ferster, Giulia Da Poian, Kiran Menachery, Simon J., Schreiner, Caroline Lustenberger, Angelina Maric, Reto Huber, Christian, Baumann, and Walter Karlen

TL;DR
This study introduces and benchmarks two novel real-time EEG phase estimation algorithms, PLL and PV, for auditory stimulation during sleep, demonstrating their effectiveness in low-amplitude slow waves using wearable devices.
Contribution
The paper presents two new algorithms, PLL and PV, for real-time phase estimation of sleep EEG, optimized for wearable devices and low-amplitude slow waves, outperforming existing methods.
Findings
Both algorithms achieved over 70% of stimulation during the up-phase.
PV outperformed in low-amplitude and higher frequency SW detection.
Algorithms maintained low computational load on wearable hardware.
Abstract
Auditory stimulation of EEG slow waves (SW) during non-rapid eye movement (NREM) sleep has shown to improve cognitive function when it is delivered at the up-phase of SW. SW enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration such as Parkinson disease (PD). However, existing algorithms to estimate the up-phase suffer from a poor phase accuracy at low EEG amplitudes and when SW frequencies are not constant. We introduce two novel algorithms for real-time EEG phase estimation on autonomous wearable devices. The algorithms were based on a phase-locked loop (PLL) and, for the first time, a phase vocoder (PV). We compared these phase tracking algorithms with a simple amplitude threshold approach. The optimized algorithms were benchmarked for phase accuracy, the capacity to estimate phase at SW amplitudes…
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Taxonomy
TopicsEEG and Brain-Computer Interfaces · Sleep and Wakefulness Research · Vestibular and auditory disorders
