A Robust Image Encryption Protocol for Secure Data Sharing in Brain Computer Interface Applications

Brain-computer interface (BCI) technology has emerged as a transformative means to link human neural activity with electronic devices. BCIs, which facilitate bidirectional communication between the brain and computers, are categorized as invasive, semi-invasive, and non-invasive. EEG (electroencephalography), a non-invasive technique recorded via electrodes placed on the scalp, serves as the primary data source for BCI systems. P300, a component of the human brain’s event-related potential, has gained prominence for detecting cognitive responses to stimuli. However, the susceptibility of BCI data to tampering during transmission underscores the critical need for robust security and privacy measures. To address security issues in P300-based BCI systems, this article introduces a novel elliptic curve-based certificateless encryption (CLE) technique integrated with image encryption protocols to safeguard the open communication pathway between near control and remote control devices. Our approach, unique in its exploration of ECC-based encryption for these systems, offers distinct advantages in security, demonstrating high accuracy in preserving data integrity and confidentiality. The security of our proposed scheme is rigorously validated using the Random Oracle Model. Simulations conducted using MATLAB evaluate the proposed image encryption protocol both theoretically and statistically, showing strong encryption performance against recent methods. Results include an entropy value of 7.98, Unified Average Changing Intensity (UACI) of 33.4%, Normalized Pixel Change Rate (NPCR) of 99.6%, and negative correlation coefficient values, indicating efficient and effective encryption and decryption processes.