EEG subject detection : opportunity or threat?

Brain-computer interfaces (BCI) leverage neurophysiological features like Electroencephalography (EEG) to enhance user-computer interaction. EEG's high temporal resolution and unobtrusiveness make it ideal for BCI systems, facilitating real-time interaction with minimal latency. Prior studies employed EEG across domains, including Health and Security. Researchers expanded EEG data's potential within Natural Language Processing and Information Retrieval (IR), facilitated by machine learning methods. While the primary intention of using EEG has been to enhance the search experience, the nature of EEG data collection may capture sensitive information about the subjects, such as their identities, beyond the intended use. This raises ethical and privacy concerns which have yet to be addressed. This work explores the detection of participants' identities from their EEG. A case study was formulated, incorporating EEG data from 40 participants engaging in the single-session simultaneous capacity (SIMKAP) experiment. Each participant's EEG recordings were utilised to perform subject classification. Results show that deep learning models identify 40 individual subjects that can be revealed with an accuracy of up to 58.8% (S.D. 5%). Based on these findings, the benefits of EEG within IR, ethical dilemmas presented by its use, and potential solutions must be addressed before wider development and implementation of further EEG-IR systems.