Protection Against Person-Identification from EEG Patterns: A Blockchain-based Approach

Monitoring brain cognition functions with the help of wearables like Electroencephalography (EEG) sensors has recently become a popular assistive technology for the Internet of Medical Things (IoMT). EEG is popularly established as a safe, practical, and portable IoMT device. From monitoring stimuli responses to facilitating critical diagnosis, EEG is highly effective in clinical, research, and home settings. Nonetheless, the brain patterns of EEG linked with a person can be easily verifiable through backtracking and is a threat to the person's identity. Hence, privacy preservation of EEG data is critical, implying we need to keep it confidential and free from susceptibility. We propose a Blockchain-based access control mechanism for users to manage their EEG data and share it imperatively. We consider a fully homomorphic encryption method for the dynamic data exchange between authorized users and doctors within a private Blockchain network. The decentralized application (DApp) uses lightweight smart contracts to process data at the source and optimize the on-chain storage. Moreover, our proposed methodology adequately tackles privacy preservation by enabling users to control their personal information directly, building trust, and promoting more extensive acceptance.