Measuring hearing instrument sound modification using integrated ear-EEG
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.
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
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