Measuring hearing instrument sound modification using integrated ear-EEG

Authors

  • Florian Denk Medizinische Physik, University of Oldenburg, Oldenburg, Germany; Cluster of Excellence Hearing4all, Oldenburg, Germany http://orcid.org/0000-0003-3490-123X
  • Marleen Grzybowski Medizinische Physik, University of Oldenburg, Oldenburg, Germany; Cluster of Excellence Hearing4all, Oldenburg, Germany
  • Stephan M. A. Ernst Medizinische Physik, University of Oldenburg, Oldenburg, Germany; Cluster of Excellence Hearing4all, Oldenburg, Germany; University Hospital Gießen and Marburg, Germany
  • Birger Kollmeier Medizinische Physik, University of Oldenburg, Oldenburg, Germany; Cluster of Excellence Hearing4all, Oldenburg, Germany
  • Stefan Debener Neuropsychology Group, University of Oldenburg, Oldenburg, Germany; Cluster of Excellence Hearing4all, Oldenburg, Germany
  • Martin G. Bleichner Neuropsychology Group, University of Oldenburg, Oldenburg, Germany; Cluster of Excellence Hearing4all, Oldenburg, Germany http://orcid.org/0000-0001-6933-9238

Keywords:

ear-EEG, Hearing Aid, AEP

Abstract

We integrated ear electrodes into a live hearing system and evaluated the feasibility of recording electroencephalography (EEG) features with this setup using an auditory discrimination experiment. The long-term goal is to construct a closed-loop brain-computer-interface that is integrated in a mobile research hearing system. Here, the EEG setup consists of 3 electrodes embedded in the earmoulds of an experimental hearing system and 10 flex-printed electrodes positioned around each ear, all connected to a wireless EEG amplifier. Four consecutive identical broadband stimuli were played in headphones while the spectral profile of sounds arriving at the eardrum was altered by switching the signal processing setting of the hearing system. Such switches were made between presentation of the third and the fourth stimulus, in half of all epochs. Seventeen normal hearing subjects participated and were instructed to indicate whether the last stimulus sounded different. The behavioural data verified clear audibility of the switches. The EEG analysis revealed differences between switch and no-switch trials in the N1 and P3 latency range. Importantly, changes in the spectral content of the noise floor of the hearing device were already sufficient to elicit these responses. These results confirm that stimulus-related brain signals acquired from ear-EEG during real-time audio processing can be successfully derived.

References

Bleichner, M.G., Lundbeck, M., Selisky, M., Minow, F., et al. (2015). “Exploring miniaturized EEG electrodes for brain-computer interfaces. An EEG you do not see?” Physiol. Rep., 3, e12362. doi: 10.14814/phy2.12362

Bleichner, M.G., Mirkovic, B., and Debener, S. (2016). “Identifying auditory attention with ear-EEG: cEEGrid versus high-density cap-EEG comparison,” J. Neural Eng., 13, 066004. doi: 10.1088/1741-2560/13/6/066004.

Bleichner, M.G., and Debener, S. (2017). “Concealed, unobtrusive ear-centered EEG acquisition: cEEGrids for transparent EEG,” Front. Hum. Neurosci., 11, doi: 10.3389/fnhum.2017.00163

Debener, S., Emkes, R., De Vos, M., and Bleichner, M. (2015). “Unobtrusive ambulatory EEG using a smartphone and flexible printed electrodes around the ear,” Sci. Rep., 5, 16743. doi: 10.1038/srep16743.

Delorme, A., and Makeig, S. (2004). “EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis”, J Neurosci. Methods 134, 9-21, doi: 10.1016/j.jneumeth.2003.10.009

Denk, F., Hiipakka, M., Kollmeier, B., and Ernst, S.M.A. (2017). “An individualised acoustically transparent earpiece for hearing devices,” Int. J. Audiol., 1-9. doi:10.1080/14992027.2017.1294768.

Doclo, S., Kellermann, W., Makino, S., and Nordholm, S.E., (2015). “Multichannel signal enhancement algorithms for assisted listening devices: Exploiting spatial diversity using multiple microphones,” IEEE Sig. Proc. Mag., 32, 18-30. doi: 10.1109/MSP.2014.2366780

Grimm, G., Herzke, T., Berg, D., and Hohmann, V. (2006). “The master hearing aid: a PC-based platform for algorithm development and evaluation,” Acta Acust. United Ac., 92, 618-628.

Kothe, C., Schwartz Centre for Computational Neuroscience (2015). “Lab Streaming Layer (LSL)”. Available at https://github.com/sccn/labstreaminglayer.

Kidmose, P., Looney, D., and Mandic, P. (2012). “Auditory evoked responses from ear-EEG recordings,” Proc. IEEE EMBS, San Diego, USA, 586-589. doi: 10.1109/EMBC.2012.6345999.

Looney, D., Park, C., Kidmose, P., Rank, M.L., Ungstrup, M., Rosenkranz, K., and Mandic, D.P. (2011). “An in-the-ear platform for recording electroencephalogram,” Proc. IEEE EMBS, Boston, USA, 6882-6885. doi: 10.1109/IEMBS.2011.6091733

Meyer, B.T., Jürgens, T., Wesker, T., Brand, T., and Kollmeier, B. (2010). “Human phoneme recognition depending on speech-intrinsic variability,” J Acoust. Soc. Am., 128, 3126-3141. doi: 10.1121/1.3493450

Mikkelsen, K.B., Kappel, S.L., Mandic, D.P., and Kidmose, P. (2015). “EEG recorded from the ear: Characterizing the Ear-EEG Method,” Front. Neurosci., 9, doi: 10.3389/fnins.2015.00438

Mirkovic, B., Bleichner, M.G., De Vos, M., and Debener, S., (2016). “Target speaker detection with concealed EEG around the ear,” Front. Neurosci., 10, doi: 10.3389/fnins.2016.00349

O’Sullivan, J.A., Power, A.J., Mesgarani, N., Rajaram, S., et al. (2015). “Attentional selection in a cocktail party environment can be decoded from single-trial EEG,” Cereb. Cortex, 25, 1697-1706. doi: 10.1093/cercor/bht355

Additional Files

Published

2018-01-02

How to Cite

Denk, F., Grzybowski, M., Ernst, S. M. A., Kollmeier, B., Debener, S., & Bleichner, M. G. (2018). Measuring hearing instrument sound modification using integrated ear-EEG. Proceedings of the International Symposium on Auditory and Audiological Research, 6, 351–358. Retrieved from https://proceedings.isaar.eu/index.php/isaarproc/article/view/2017-43

Issue

Section

2017/6. Advances in hearing-instrument features and related effects