  Study Objectives: Polysomnography (PSG) currently serves as the benchmark for
evaluating sleep disorders. Its discomfort makes long-term monitoring
unfeasible, leading to bias in sleep quality assessment. Hence, less invasive,
cost-effective, and portable alternatives need to be explored. One promising
contender is the in-ear-EEG sensor. This study aims to establish a methodology
to assess the similarity between the single-channel in-ear-EEG and standard PSG
derivations.
  Methods: The study involves four-hour signals recorded from ten healthy
subjects aged 18 to 60 years. Recordings are analyzed following two
complementary approaches: (i) a hypnogram-based analysis aimed at assessing the
agreement between PSG and in-ear-EEG-derived hypnograms; and (ii) a
feature-based analysis based on time- and frequency- domain feature extraction,
unsupervised feature selection, and definition of Feature-based Similarity
Index via Jensen-Shannon Divergence (JSD-FSI).
  Results: We find large variability between PSG and in-ear-EEG hypnograms
scored by the same sleep expert according to Cohen's kappa metric, with
significantly greater agreements for PSG scorers than for in-ear-EEG scorers (p
< 0.001) based on Fleiss' kappa metric. On average, we demonstrate a high
similarity between PSG and in-ear-EEG signals in terms of JSD-FSI (0.79 +/-
0.06 -awake, 0.77 +/- 0.07 -NREM, and 0.67 +/- 0.10 -REM) and in line with the
similarity values computed independently on standard PSG-channel-combinations.
  Conclusions: In-ear-EEG is a valuable solution for home-based sleep
monitoring, however further studies with a larger and more heterogeneous
dataset are needed.
