that automatically determines appropriate sleep stages from
LIBS’s outputs acquired in sleep studies as its application.
Generally, the whole-night sleep staging system, as illustrated
in Figure 2, consists of three following primary modules.
2. 1. Signal acquisition
Overall, this module focuses on tackling our first challenge that
requires ( 1) an ability to adapt to the small uneven area inside
human ear and its easy deformability under the jaw movements
(e.g., teeth grinding, chewing, and speaking), ( 2) a potential to
acquire the naturally weak biosignals, which have micro-Volt
amplitude, and ( 3) a provision of comfortable and harmless
wearing to the users. We fulfill these obstacles by firstly custom-making a deformable earplug-like sensors using a viscoelastic
material with atop sensitive electrodes using several layers of
thin, soft, and highly conductive materials. To possibly capture
the weak biosignals from inside human ears, we then increase
the distance between the main electrodes and the reference
point to further enhance signal fidelity. Finally, we preprocess
the collected signal to eliminate signal interference (e.g., body
movement artifact and electrical noise).
2. 2. In-ear mixed signal separation
In this module, we form a supervised algorithm to overcome
our second challenge for signal separation. This challenge,
in detail, is related to ( 1) overlapping characteristics of three
signals in both time and frequency domains, ( 2) a random
activation of the sources generating them, and ( 3) their variation from person to person and in different recordings. We
solve these problems by developing a supervised Nonnegative
Matrix Factorization (NMF)-based model that can separate
the preprocessed in-ear mixed signal into EEG, EOG, and
EMG with high similarity to the ground truth given by the
gold-standard device. Specifically, our separation algorithm
initially learns prior knowledge of the biosignals of interest
through their individual spectral templates. It then adapts
the templates to the variation between people through a
deformation step. Hence, the model we build can alter itself
slightly to return the best fit between the expected biosignals and the given templates.
2. 3. Automatic sleep staging
This last module provides a set of machine learning algo-
rithms to continuously score sleep into appropriate sleep
stages using EEG, EOG, and EMG separated from the in-
ear mixed signal. Because those signals can have similar
However, as minimizing the number of used electrodes, we
can achieve only the single-channel signal, which is a mix-
ture of EEG, EOG, EMG signals, and unwanted noise. We then
develop a signal separation model for LIBS to extract the three
signals of interest from the in-ear mixed signal. To validate the
lossless of essential physiological information in the separated
signals acquired by LIBS, we finally develop a sleep stage classi-
fication algorithm to score every 30sec epoch of the separated
signals into an appropriate stage using a set of discriminative
features obtained from them. Through the hardware prototype
and a one-month long user study, we demonstrated that the
proposed LIBS was comparable to the existing dedicated sleep
assessment system (i.e., PSG) in terms of accuracy.
Due to the structural variation across ear canals and overlapped characteristics of the EEG, EOG, and EMG signals,
building LIBS is difficult because of three following key reasons. ( 1) The brain signal is quite small in order of micro-Volts (µV). Additionally, the human head anatomy shown in
Figure 1(b) indicates that their sources are not too close to
the location of LIBS placed in the ear canals to be sensed,
especially in case of the weak brain source, ( 2) The characteristics of those three biosignals are overlapped in both
time and frequency domains. Moreover, their activation is
random and possibly simultaneous during the monitoring period, and ( 3) The signal quality is easily varied by the
displacement of electrodes across device hookups and the
variation of physiological body conditions across people.
Consequently, our first challenge is to build sensors capable
of providing a high level of sensitivity while recording the
biosignals from afar and comfort while wearing the device.
Our second challenge is then to provide a robust separation
mechanism in the presence of multiple variances, which
becomes a significant hurdle.
While addressing the above challenges to realizing LIBS,
we make the following contributions through this work:
1. Developing a light-weight and low-cost earplug-like
sensor with highly sensitive and soft electrodes, the
whole of which is comfortably and safely placed inside
human ears to continuously measure the voltage potential of the biosignals in long term with high fidelity.
2. Deriving and implementing a single-channel signal
separation model, which integrates a process of learning source-specific prior knowledge for adapting the
extraction of EEG, EOG, and EMG from the mixed in-ear signal to suit the variability of the signals across
people and recordings.
3. Developing an end-to-end sleep staging system, which
takes the input of three separated biosignals and automatically determines the appropriate sleep stages, as a
proof-of-concept of LIBS’s potential in reality.
4. Conducting an over 30 day long user studies with eight
subjects to confirm the feasibility and learn the usability of LIBS.
2. LIBS’S SYSTEM OVERVIEW
In this section, we present an overall design of LIBS in order
to achieve the EEG, EOG, and EMG signals individually from
the in-ear mixed biosignal. Additionally, we provide a module
Figure 2. LIBS architecture and its sleep staging application.
Pure silver leaf
Conductive adhesive gel
Stage W Stage N1 Stage N2 Stage N3 Stage REM
EEG signal EOG signal EMG signal