An IoT-enabled EEG headphones with customized music for chronic tinnitus assessment and symptom management

Abstract

Chronic tinnitus often affects elderly or hearing-impaired individuals, which can disturb their daily lives by disrupting concentration and limiting communication. Clinically, sound masking using external sounds like white noise (WN) aims to mask tinnitus and relieve secondary symptoms. Even when symptoms are relieved, tinnitus often requires long-term management, and for patients to visit healthcare professionals regularly. Generally, it could make maintaining symptom management challenging due to the time and effort required for consistent follow-ups. EEG is considered as one of the objective marker for assessing tinnitus symptoms. In this study, we designed IoT-enabled EEG sensing (IEES) headphones, an innovative IoT device that provided customized music (CM) and EEG measurement. The headphones employed a pitch-matching (PM) method to create CM tailored to each patient at specific frequencies for tinnitus patients. To collect EEG measurements, the device incorporated OpenBCI electrodes and a sensing chip to monitor brain waves and evaluate the outcomes.. After 30 days of experiment, participants showed significant reductions in both tinnitus handicap inventory (THI) scores and visual analog scale for annoyance (VAS-A) scores. In comparison, tinnitus frequency showed a slight reduction. EEG measurements demonstrated an increase in alpha band activity. In questionnaires, patients reported high satisfaction with their experiences. These findings highlight the potential of the proposed method for chronic tinnitus assessment and symptom management.