Event-Related Potentials Measured From In and Around the Ear Electrodes Integrated in a Live Hearing Device for Monitoring Sound Perception

Future hearing devices could exploit brain signals of the user derived from electroencephalography (EEG) measurements, for example, for fitting the device or steering signal enhancement algorithms. While previous studies have shown that meaningful brain signals can be obtained from ear-centered EEG electrodes, we here present a feasibility study where ear-EEG is integrated with a live hearing device. Seventeen normal-hearing participants were equipped with an individualized in-the-ear hearing device and an ear-EEG system that included 10 electrodes placed around the ear (cEEGrid) and 3 electrodes spread out in the concha. They performed an auditory discrimination experiment, where they had to detect an audible switch in the signal processing settings of the hearing device between repeated presentations of otherwise identical stimuli. We studied two aspects of the ear-EEG data: First, whether the switches in the hearing device settings can be identified in the brain signals, specifically event-related potentials. Second, we evaluated the signal quality for the individual electrode positions. The EEG analysis revealed significant differences between trials with and without a switch in the device settings in the N100 and P300 range of the event-related potential. The comparison of electrode positions showed that the signal quality is better for around-the-ear electrodes than for in-concha electrodes. These results confirm that meaningful brain signals related to the settings of a hearing device can be acquired from ear-EEG during real-time audio processing, particularly if electrodes around the ear are available.

[1]  Kaare B. Mikkelsen,et al.  EEG Recorded from the Ear: Characterizing the Ear-EEG Method , 2015, Front. Neurosci..

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

[3]  Henrik Møller,et al.  Transfer characteristics of headphones measured on human ears , 1995 .

[4]  Preben Kidmose,et al.  A Study of Evoked Potentials From Ear-EEG , 2013, IEEE Transactions on Biomedical Engineering.

[5]  J. Polich Updating P300: An integrative theory of P3a and P3b , 2007, Clinical Neurophysiology.

[6]  Matti Karjalainen,et al.  Modeling the External Ear Acoustics for Insert Headphone Usage , 2010 .

[7]  E Gordon,et al.  Does the N100 evoked potential really habituate? Evidence from a paradigm appropriate to a clinical setting. , 1992, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[8]  H. Schimmel The (�) Reference: Accuracy of Estimated Mean Components in Average Response Studies , 1967, Science.

[9]  Zhuo Chen,et al.  Neural decoding of attentional selection in multi-speaker environments without access to clean sources , 2017, Journal of neural engineering.

[10]  D. P. Mandic,et al.  The In-the-Ear Recording Concept: User-Centered and Wearable Brain Monitoring , 2012, IEEE Pulse.

[11]  Andreas Büchner,et al.  Toward Automated Cochlear Implant Fitting Procedures Based on Event-Related Potentials , 2017, Ear and hearing.

[12]  L. Hedges Distribution Theory for Glass's Estimator of Effect size and Related Estimators , 1981 .

[13]  T. Picton,et al.  The N1 wave of the human electric and magnetic response to sound: a review and an analysis of the component structure. , 1987, Psychophysiology.

[14]  John J. Foxe,et al.  Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG. , 2015, Cerebral cortex.

[15]  S. Debener,et al.  Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear , 2015, Scientific Reports.

[16]  B. Kollmeier,et al.  Human phoneme recognition depending on speech-intrinsic variability. , 2010, The Journal of the Acoustical Society of America.

[17]  Stefan Debener,et al.  Identifying auditory attention with ear-EEG: cEEGrid versus high-density cap-EEG comparison , 2016, Journal of neural engineering.

[18]  S. Debener,et al.  Concealed, Unobtrusive Ear-Centered EEG Acquisition: cEEGrids for Transparent EEG , 2017, Front. Hum. Neurosci..

[19]  Brian C. J. Moore,et al.  Future Directions for Hearing Aid Development , 2016 .

[20]  Birger Kollmeier,et al.  An individualised acoustically transparent earpiece for hearing devices , 2018, International journal of audiology.

[21]  Martin G Bleichner,et al.  Exploring miniaturized EEG electrodes for brain-computer interfaces. An EEG you do not see? , 2015, Physiological reports.

[22]  Stefan Debener,et al.  Target Speaker Detection with Concealed EEG Around the Ear , 2016, Front. Neurosci..

[23]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[24]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[25]  Giso Grimm,et al.  The master hearing Aid : A PC-based platform for algorithm development and evaluation , 2006 .

[26]  P. Lachenbruch Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .