Research Engineer / Data Scientist for Tinnitus Classification in EEG

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• Tinnitus, i.e., the perception of sound in the absence of an external acoustic stimulus, is a widespread disease. Severe forms of tinnitus can substantially impair quality of life, leading to psychological complaints similar to chronic neuropathic pain, including insomnia, anxiety, and depression. The lack of treatment options is frustrating for patients and clinicians alike. Although tinnitus is a complex condition, only qualitative subjective testing (e.g., questionnaires) is performed for diagnostics because of the lack of objective methods. These examinations are not sufficient to characterize the tinnitus in detail.
• This project wants to demonstrate the applicability of objective tinnitus diagnostics. Currently, there is no technology available that enables to derive tinnitus measures without subjective/categorical responses of tinnitus subjects. Our approach combines the collection of EEG data with the administration of specific acoustic stimuli that enable a temporary tinnitus suppression. The approach is highly innovative as it provides data for within-subject comparison, mitigating the limitations of classification algorithms associated with the otherwise large heterogeneity of EEG measurements.
• This project aims to develop algorithms that automatically identify/classify objective EEG biomarkers for tinnitus. The EEG Data is already recorded and a conventional power-band based analysis that demonstrates the applicability of the approach is already completed. The main task of the engineer is to apply machine learning/intelligence to automate the classification procedure. The project is performed as a collaboration between the Inselspital Bern, Department of Otorhinolaryngology (Prof. Dr. Marco Caversaccio) and the Experimental Audiology research group at the Technical University of Munich under the supervision of Prof. Dr. Wilhelm Wimmer.

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