  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 between 20 and
60 microV, and SW frequencies above 1 Hz on 324 recordings from healthy older
adults and PD patients. Furthermore, the algorithms were implemented on a
wearable device and the computational efficiency and the performance was
evaluated on simulated sleep EEG, as well as prospectively during a recording
with a PD patient. All three algorithms delivered more than 70% of the
stimulation triggers during the SW up-phase. The PV showed the highest capacity
on targeting low-amplitude SW and SW with frequencies above 1 Hz. The testing
on real-time hardware revealed that both PV and PLL have marginal impact on
microcontroller load, while the efficiency of the PV was 4% lower than the PLL.
Active auditory stimulation did not influence the phase tracking. This work
demonstrated that phase-accurate auditory stimulation can be delivered during
home-based sleep interventions with a wearable device also in populations with
low-amplitude SW.

Signal Processing;Systems and Control;Systems and Control