Tinnitus is the perception of sound in the absence of an external acoustic stimulus, and can sound like beeping, white noise and ringing. Although the triggering event can be acute inner ear damage, the chronic condition is established through subsequent brain remodeling. These neurological changes result in sustained, abnormal neuronal activity, much of which remains poorly understood. The symptoms of tinnitus are highly variable between patients, and severe forms of tinnitus can substantially impair quality of life by affecting concentration, sensory perception and sleep. The prevalence of tinnitus is estimated to be 10-15% of the general population and is expected to increase due to demographic developments including ageing. The variability of symptoms requires objective and patient-specific tinnitus assessment and classification technology as well as personalized tinnitus treatments. This would allow clinicians to quantify treatment outcomes of existing interventions and facilitate the development of novel therapies.
One such approach to objective tinnitus assessment is the identification of neuronal correlates in electroencephalography (EEG). The Hearing Research Laboratory (HRL) and the Ophthalmic Technology Laboratory are investigating statistical approaches and computational modelling to extract such neuronal correlates using EEG data, aiming to derive patient-specific “hearing fingerprints” in the future. Through these, clinicians can more accurately diagnose tinnitus by combining objective EEG measurements with subjective, psychological patient assessment batteries. In addition, the HRL is developing software systems or “Apps” for use on smart phones for tinnitus self-assessment, further empowering patients to lead on their own therapy pathway. This patient-centered approach to tinnitus aims to deepen clinical assessment datasets from snapshot measurement under quiet conditions and combine them with continuous long-term self-monitoring of the symptoms under more “life-like” conditions.