Biometric traits are unique characteristics of an individual’s body or behavior that can be used for identification and authentication. Biometric authentication uses unique physiological and behavioral traits for secure identity verification. Traditional unimodal biometric authentication systems often suffer from spoofing attacks, sensor noise, forgery, and environmental dependencies. To overcome these limitations, our work presents multimodal biometric authentication integrated with the characteristics of electroencephalograph (EEG) signals and handwritten signatures to enhance security, efficiency, and robustness. EEG-based authentication uses the brainwave patterns’ intrinsic and unforgeable nature, while signature recognition demonstrates an additional behavioral trait for effectiveness. Our system processes EEG data of an individual with 14-channel readings, and the signature with the images ensures a seamless fusion of both modalities.Combining physiological and behavioral biometrics, our approach will significantly decrease the risk of unimodal authentication, including forgery, spoofing, and sensor failures. Our system, evaluated on a dataset of 30 subjects with genuine and forged data, demonstrates a 97% accuracy. Designed for small organizations, the modular structure, low computation algorithms, and simplicity of the hardware promote deployment scalability.