Your brain's state before lights-out hints at how well stimulation will work
In a laboratory study of 28 healthy adults, researchers recorded EEG before bedtime and then gave participants either real auditory stimulation or a sham (inactive) version on separate nights. Stimulation increased slow-wave activity in more than 90% of participants, but the size of that increase varied. Higher pre-sleep alpha power and theta power were both associated with larger slow-wave enhancement, and so was a longer sleep onset latency, meaning the time it took to fall asleep [2]. These are correlations of moderate strength, not perfect predictors, but they point to a measurable pre-sleep state that makes the sleeping brain more receptive.
The same study went further and used machine learning on just eight minutes of pre-sleep EEG to classify who would respond strongly. That model reached about 80% accuracy, with an AUC of 0.93, a score where 1.0 would be perfect and 0.5 would be chance [2]. The practical takeaway is that a short recording before sleep may be enough to flag likely responders, though this was a small sample under mild sleep restriction, so the exact accuracy should be treated as promising rather than settled.
Why a slower slide into sleep may mean a more stimulable brain
The link between longer sleep onset latency and greater slow-wave enhancement is a little counterintuitive, since taking longer to fall asleep is often framed as a problem. One plausible reading is that the pre-sleep brain state captured by alpha and theta power and by sleep-onset timing reflects how easily the cortex can be pushed into synchronized slow oscillations once sleep begins [2]. The study's authors frame it as pre-sleep brain state determining responsiveness, and suggest tailoring stimulation to these features could optimize receptivity [2]. That is a proposed strategy rather than a proven clinical protocol.
The shape of the ongoing slow wave also matters, pulse by pulse
Baseline features are not the only lever. Even within a night, the morphology, or shape, of the ongoing slow oscillation carries information about whether a given sound will successfully enhance that oscillation [1]. In other words, two people with similar pre-sleep profiles may still differ in how many individual stimulation pulses actually land, depending on the moment-to-moment EEG signal the system is tracking.
This connects to work on how stimulation produces its effect at all. Magnetoencephalography recordings suggest sound reaches ventral frontal regions through non-lemniscal pathways, and that the excitability of ventral frontal tissue, more than auditory cortex, is critical for generating the slow oscillation that follows [3]. If frontal state governs whether a slow oscillation is generated, it is reasonable that both pre-sleep frontal activity and the ongoing oscillation's shape would shape outcomes, though the predictive studies and the mechanism study were separate and did not test this link directly.
What a curious reader should take away
For now, the strongest evidence for predicting response comes from pre-sleep spectral power (alpha and theta) and sleep onset latency, measured in a small laboratory sample [2], plus the predictive value of ongoing slow-oscillation features during sleep [1]. Not every study of closed-loop stimulation speaks to this question: some focus on whether the technique works at all, such as a trial that found no significant difference in memory retention or sleep quality between real and sham nights, where less noisy slow-oscillation signals were linked to better detection and stimulation [5], and others focus on hardware, showing that in-ear sensors can detect slow oscillations well enough to drive stimulation [4]. Those results tell us about feasibility and average effects, not about who responds best.
The honest summary is that response prediction is early-stage. The pre-sleep model's high accuracy comes from one modest study, the oscillation-shape predictor comes from a separate line of work, and no study here shows that selecting people by these features actually improves sleep or memory outcomes. The features are best read as promising signals of receptivity, not yet as a ready-made screening test.
Sources used in this answer
Ongoing neural oscillations predict the post-stimulus outcome of closed loop auditory stimulation during slow-wave sleep
Morphological features of ongoing slow oscillations in the EEG predict the post-stimulus outcome of closed-loop auditory stimulation during slow-wave sleep.
Bedtime Brain State Predicts the Impact of Closed-Loop Auditory Stimulation on Sleep and Cognition
Pre-sleep alpha and theta power and longer sleep onset latency correlated with greater slow-wave enhancement from auditory stimulation, and eight minutes of pre-sleep EEG predicted individual responsiveness with about 80% accuracy (AUC 0.93).
The neurophysiology of closed‐loop auditory stimulation in sleep: A magnetoencephalography study
Magnetoencephalography suggests auditory stimulation reaches ventral frontal regions via non-lemniscal pathways, and the excitability of ventral frontal tissue is most critical for generating the resulting slow oscillation.
Auditory closed‐loop stimulation on sleep slow oscillations using in‐ear EEG sensors
In-ear EEG provided enough information to detect slow oscillations in real time and successfully drive auditory closed-loop stimulation that enhanced scalp slow oscillations.
THE RELATIONSHIP BETWEEN CLOSED-LOOP AUDITORY STIMULATION DURING SLEEP AND EEG ON MEMORY CONSOLIDATION
Real versus sham closed-loop auditory stimulation produced no significant difference in memory retention or subjective sleep quality, though less noisy slow-oscillation signals correlated with greater detection and stimulation.