An EEG signal encryption algorithm based on dual-composite IT-ICMIC chaotic map and adaptive non-uniform partition

Abstract

Electroencephalogram (EEG) signals have become critical in applications such as medical diagnostics and neurofeedback systems. However, its sensitivity makes it vulnerable to unauthorized access and the risk of data leakage. To address these challenges, this study proposes an EEG signal encryption method based on a dual composite Inverse Trigonometric Iterative Chaotic Map (IT-ICMIC) and adaptive non-uniform partition. To enhance the security of EEG signal encryption, the dual composite IT-ICMIC chaotic map is introduced, addressing the limitations of traditional single chaotic maps in complexity and unpredictability. Additionally, the adaptive non-uniform partition algorithm explores the intrinsic dynamic characteristics of EEG signals. Further, the mined features integrate with the fundamental properties of EEG signals to generate chaotic sequences, enabling efficient and robust encryption. Extensive experiments and security analysis demonstrate that the proposed method achieves superior performance for EEG signal encryption, with an average NSCR of 100, a UACI of 33.36 highlighting its strong encryption effectiveness.