Abstract
Introduction
This study aims to see if the PNFS has an effect on sleep
parameters. Sleep deprivation has significant adverse effects
(e.g., Bolge, Doan, Kannan, & Baran, 2009; Gustavsson et al.,
2011). Sleep medication, although improving sleep, can have
adverse effects on daytime functioning (e.g., Verster, Veldhuijzen,& Volkerts, 2004). The PNFS may present an alternative
method
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to improve sleep quality, by means of Audio EEG
neurofeedback. This is used as an relaxation technique; participants
are rewarded when the EEG signal indicates relaxation.
Hypotheses/research questions
The research questions are whether subjectively reported sleep
parameters (PSQI, TWT, NAWand SOL) improve as the PNFS
is used, and whether the SMR power increased most and the
beta power decreased most, when the audio neurofeedback
condition was aiming to achieve this. The hypotheses are that
they can be answered in the positive.
Method
60 healthy participants will be included. Neurofeedback training
is performed at home, in the evening, for half an hour, at
least 21 days. Participants are equally divided among 3 groups.
In the SMR up group, participants are rewarded with inclusion
of bass-frequencies in their favourite music when they have
relatively high SMR power. In the Beta down group participants
receive this reward when beta power decreases. In the
Placebo group the participants are given a random sample from
previous recordings. Results are analysed using linear mixed
effects models (Bates, M¨achler, Bolker, & Walker, 2015).
Results
Only the results from the first 30 participants are presented in
this paper. Only the PSQI decreased significantly more in the
training groups than in the Placebo group. The EEG results
show that the SMR power at the start of each session increased
the least in the SMR up group. The beta power at the start of
each session decreased the most in the SMR up group. Each
consecutive session, participants in the Beta down group do
get faster at decreasing beta power. In the SMR up group, participants
get faster at increasing the SMR power. For both
groups there were significant differences in baseline coefficients.
As a result, it is unclear whether baseline differences
between groups caused the improvements, or the intervention.
Conclusion
Further analyses is required, before definite conclusions can
be drawn. The models for the EEG power should include additional
random effects, in order to control for baseline differences.
Moreover, the relation between the subjective response
variables and the EEG power should be assessed. The screens
in the app that potentially provide visual neurofeedback should
be blocked during neurofeedback training. The use of the
Health Watch in Phase 2 may provide further insight in the
relation between objective sleep measures and neurofeedback,
additional questionnaires might include response variables that
are influenced by the neurofeedback.
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