Literature DB >> 28269548

Ear-EEG allows extraction of neural responses in challenging listening scenarios - A future technology for hearing aids?

L Fiedler, J Obleser, T Lunner, C Graversen.   

Abstract

Advances in brain-computer interface research have recently empowered the development of wearable sensors to record mobile electroencephalography (EEG) as an unobtrusive and easy-to-use alternative to conventional scalp EEG. One such mobile solution is to record EEG from the ear canal, which has been validated for auditory steady state responses and discrete event related potentials (ERPs). However, it is still under discussion where to place recording and reference electrodes to capture best responses to auditory stimuli. Furthermore, the technology has not yet been tested and validated for ecologically relevant auditory stimuli such as speech. In this study, Ear-EEG and conventional scalp EEG were recorded simultaneously in a discrete-tone as well as a continuous-speech design. The discrete stimuli were applied in a dichotic oddball paradigm, while continuous stimuli were presented diotically as two simultaneous talkers. Cross-correlation of stimulus envelope and Ear-EEG was assessed as a measure of ongoing neural tracking. The extracted ERPs from Ear-EEG revealed typical auditory components yet depended critically on the reference electrode chosen. Reliable neural-tracking responses were extracted from the Ear-EEG for both paradigms, albeit weaker in amplitude than from scalp EEG. In conclusion, this study shows the feasibility of extracting relevant neural features from ear-canal-recorded "Ear-EEG", which might augment future hearing technology.

Mesh:

Year:  2016        PMID: 28269548     DOI: 10.1109/EMBC.2016.7592020

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  10 in total

1.  Neural decoding of attentional selection in multi-speaker environments without access to clean sources.

Authors:  James O'Sullivan; Zhuo Chen; Jose Herrero; Guy M McKhann; Sameer A Sheth; Ashesh D Mehta; Nima Mesgarani
Journal:  J Neural Eng       Date:  2017-08-04       Impact factor: 5.379

2.  Comparing In-ear EOG for Eye-Movement Estimation With Eye-Tracking: Accuracy, Calibration, and Speech Comprehension.

Authors:  Martin A Skoglund; Martin Andersen; Martha M Shiell; Gitte Keidser; Mike Lind Rank; Sergi Rotger-Griful
Journal:  Front Neurosci       Date:  2022-06-30       Impact factor: 5.152

3.  On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface.

Authors:  Soo-In Choi; Chang-Hee Han; Ga-Young Choi; Jaeyoung Shin; Kwang Soup Song; Chang-Hwan Im; Han-Jeong Hwang
Journal:  Sensors (Basel)       Date:  2018-08-29       Impact factor: 3.576

4.  Effects of Different Re-referencing Methods on Spontaneously Generated Ear-EEG.

Authors:  Soo-In Choi; Han-Jeong Hwang
Journal:  Front Neurosci       Date:  2019-08-07       Impact factor: 4.677

Review 5.  A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics.

Authors:  Grant D Searchfield; Philip J Sanders; Zohreh Doborjeh; Maryam Doborjeh; Roger Boldu; Kevin Sun; Amit Barde
Journal:  Front Digit Health       Date:  2021-08-10

6.  Evaluation of Real-Time Endogenous Brain-Computer Interface Developed Using Ear-Electroencephalography.

Authors:  Soo-In Choi; Ji-Yoon Lee; Ki Moo Lim; Han-Jeong Hwang
Journal:  Front Neurosci       Date:  2022-03-24       Impact factor: 4.677

7.  Degradation levels of continuous speech affect neural speech tracking and alpha power differently.

Authors:  Anne Hauswald; Anne Keitel; Ya-Ping Chen; Sebastian Rösch; Nathan Weisz
Journal:  Eur J Neurosci       Date:  2020-08-07       Impact factor: 3.698

8.  Neural indices of listening effort in noisy environments.

Authors:  Andrew Dimitrijevic; Michael L Smith; Darren S Kadis; David R Moore
Journal:  Sci Rep       Date:  2019-08-02       Impact factor: 4.379

9.  The Sensitivity of Ear-EEG: Evaluating the Source-Sensor Relationship Using Forward Modeling.

Authors:  Arnd Meiser; Francois Tadel; Stefan Debener; Martin G Bleichner
Journal:  Brain Topogr       Date:  2020-08-24       Impact factor: 3.020

10.  Real-Time Tracking of Magnetoencephalographic Neuromarkers during a Dynamic Attention-Switching Task.

Authors:  Alessandro Presacco; Sina Miran; Behtash Babadi; Jonathan Z Simon
Journal:  Annu Int Conf IEEE Eng Med Biol Soc       Date:  2019-07
  10 in total

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