Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.

Area of Science:

• Neuroscience
• Biometrics
• Computer Science

Background:

• Electroencephalography (EEG) is a promising biometric modality due to unique brainwave patterns.
• EEG offers resistance to spoofing attacks, enhancing user authentication security.
• Existing research on EEG-based authentication progress requires comprehensive evaluation.

Purpose of the Study:

• To provide an in-depth survey of EEG-based user authentication systems.
• To review current methodologies, challenges, and future directions in the field.
• To serve as a foundational reference for researchers in EEG biometrics.

Main Methods:

• Overview of brain structure and EEG signal acquisition principles.
• Review of EEG databases, preprocessing techniques, feature extraction, and classification algorithms.
• Identification of challenges like signal variability and the need for robust algorithms.

Main Results:

• Detailed examination of EEG signal processing for biometric authentication.
• Analysis of various feature extraction and classification strategies.
• Identification of key challenges impacting algorithm stability and robustness.

Conclusions:

• EEG biometrics presents a secure authentication method with significant potential.
• Addressing signal variability and inter-subject differences is crucial for robust systems.
• This survey provides a roadmap for future advancements in EEG-based biometric systems.