Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition

Area of Science:

• Computer Science
• Biometrics
• Neuroscience

Background:

• The digital age necessitates robust security for personal data.
• Biometrics offer a solution for secure identification and authentication.
• Electroencephalography (EEG)-based biometrics present unique advantages like universality and distinctiveness.

Purpose of the Study:

• To review existing EEG-based person recognition systems.
• To identify challenges hindering the wide-scale implementation of EEG biometrics.
• To explore future directions and potential applications for EEG recognition.

Main Methods:

• Review of proposed EEG-based person recognition systems.
• Analysis of shortcomings related to temporal stability, psychological/physiological changes, protocol design, equipment, and performance evaluation.
• Examination of future development directions and niche markets.

Main Results:

• EEG biometrics offer high potential but face significant implementation hurdles.
• Key challenges include temporal stability, user state variations, and standardization.
• Current systems require further development for reliable, large-scale deployment.

Conclusions:

• Despite challenges, advancements in EEG instrumentation, on-device processing, and machine learning are promising.
• Commercialization of EEG-based person recognition is anticipated in the near future.
• Targeted niche markets may see early adoption of these advanced biometric systems.