LIBS: a bioelectrical sensing system from human ears for staging whole-night sleep study

Sensing physiological signals from the human head has long been used for medical diagnosis, human-computer interaction, meditation quality monitoring, among others. However, existing sensing techniques are cumbersome and not desirable for long-term studies and impractical for daily use. Due to these limitations, we explore a new form of wearable systems, called LIBS, that can continuously record biosignals such as brain wave, eye movements, and facial muscle contractions, with high sensitivity and reliability. Specifically, instead of placing numerous electrodes around the head, LIBS uses a minimal number of custom-built electrodes to record the biosignals from human ear canals. This recording is a combination of three signals of interest and unwanted noise. Therefore, we design an algorithm using a supervised Nonnegative Matrix Factorization (NMF) model to split the single-channel mixed signal into three individual signals representing electrical brain activities (EEG), eye movements (EOG), and muscle contractions (EMG). Through prototyping and implementation over a 30 day sleep experiment conducted on eight participants, our results prove the feasibility of concurrently extracting separated brain, eye, and muscle signals for fine-grained sleep staging with more than 95% accuracy. With this ability to separate the three biosignals without loss of their physiological information, LIBS has a potential to become a fundamental in-ear biosensing technology solving problems ranging from self-caring health to non-health and enabling a new form of human communication interfaces.

[1]  Antoine Liutkus,et al.  Non-negative matrix factorization for single-channel EEG artifact rejection , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[2]  Joong Hoon Lee,et al.  CNT/PDMS-based canal-typed ear electrodes for inconspicuous EEG recording , 2014, Journal of neural engineering.

[3]  Suzanne Lesecq,et al.  Feature selection for sleep/wake stages classification using data driven methods , 2007, Biomed. Signal Process. Control..

[4]  Majid Sarrafzadeh,et al.  Monitoring eating habits using a piezoelectric sensor-based necklace , 2015, Comput. Biol. Medicine.

[5]  C. Alloway,et al.  The alpha attenuation test: assessing excessive daytime sleepiness in narcolepsy-cataplexy. , 1997, Sleep.

[6]  S. Chokroverty,et al.  The visual scoring of sleep in adults. , 2007, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[7]  Bhiksha Raj,et al.  Compositional Models for Audio Processing: Uncovering the structure of sound mixtures , 2015, IEEE Signal Processing Magazine.

[8]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[9]  Marina Ronzhina,et al.  Sleep scoring using artificial neural networks. , 2012, Sleep medicine reviews.

[10]  M. Littner,et al.  Practice parameters for the indications for polysomnography and related procedures: an update for 2005. , 2005, Sleep.

[11]  Farnoush Banaei Kashani,et al.  A Lightweight and Inexpensive In-ear Sensing System For Automatic Whole-night Sleep Stage Monitoring , 2016, SenSys.

[12]  U. Rajendra Acharya,et al.  Non-linear analysis of EEG signals at various sleep stages , 2005, Comput. Methods Programs Biomed..

[13]  Preben Kidmose,et al.  In-Ear EEG From Viscoelastic Generic Earpieces: Robust and Unobtrusive 24/7 Monitoring , 2016, IEEE Sensors Journal.

[14]  Slim Essid,et al.  A single-class SVM based algorithm for computing an identifiable NMF , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[15]  C. S. van der Reijden,et al.  Signal-to-noise ratios of the auditory steady-state response from fifty-five EEG derivations in adults. , 2004, Journal of the American Academy of Audiology.

[16]  Musa Peker,et al.  A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithms , 2014, Journal of Medical Systems.

[17]  Jérôme Idier,et al.  Algorithms for nonnegative matrix factorization with the beta-divergence , 2010, ArXiv.

[18]  Bart Vanrumste,et al.  Review on solving the forward problem in EEG source analysis , 2007, Journal of NeuroEngineering and Rehabilitation.

[19]  R H Margolis,et al.  A look at ear canal changes with jaw motion. , 1992, Ear and hearing.

[20]  Agata Nawrocka,et al.  Brain - Computer interface based on Steady - State Visual Evoked Potentials (SSVEP) , 2013, Proceedings of the 14th International Carpathian Control Conference (ICCC).

[21]  Mohamed Moshrefi-Torbati,et al.  Signal processing techniques applied to human sleep EEG signals - A review , 2014, Biomed. Signal Process. Control..

[22]  Jérôme Idier,et al.  Algorithms for Nonnegative Matrix Factorization with the β-Divergence , 2010, Neural Computation.