Classifying brain signals collected by wearable Internet of Things (IoT)
sensors, especially brain-computer interfaces (BCIs), is one of the
fastest-growing areas of research. However, research has mostly ignored the
secure storage and privacy protection issues of collected personal
neurophysiological data. Therefore, in this article, we try to bridge this gap
and propose a secure privacy-preserving protocol for implementing BCI
applications. We first transformed brain signals into images and used
generative adversarial network to generate synthetic signals to protect data
privacy. Subsequently, we applied the paradigm of transfer learning for signal
classification. The proposed method was evaluated by a case study and results
indicate that real electroencephalogram data augmented with artificially
generated samples provide superior classification performance. In addition, we
proposed a blockchain-based scheme and developed a prototype on Ethereum, which
aims to make storing, querying and sharing personal neurophysiological data and
analysis reports secure and privacy-aware. The rights of three main transaction
bodies - construction workers, BCI service providers and project managers - are
described and the advantages of the proposed system are discussed. We believe
this paper provides a well-rounded solution to safeguard private data against
cyber-attacks, level the playing field for BCI application developers, and to
the end improve professional well-being in the industry