EEG-based biometry using steady state visual evoked potentials.

Area of Science:

• Neuroscience
• Biometrics
• Signal Processing

Background:

• Conventional biometric measures face limitations in security and privacy.
• Brain signals, specifically electroencephalography (EEG), present a promising alternative for enhanced security.
• The distinctiveness and consistency of EEG signals across sessions are critical for reliable biometric identification.

Purpose of the Study:

• To explore the potential of using electroencephalography (EEG) signals for person recognition.
• To identify challenges in extracting robust features from EEG for biometric applications.
• To review various EEG phenomena investigated for their biometric utility.

Main Methods:

• Review of existing literature on EEG-based biometrics.
• Analysis of different EEG signal types: resting-state (eyes open/closed), visual evoked potentials (VEPs), and task-based activity.
• Focus on feature extraction techniques for distinguishing individuals.

Main Results:

• EEG-based person recognition offers potential for increased security and privacy.
• Significant challenge exists in achieving both inter-subject distinctiveness and intra-subject consistency in EEG features.
• Various EEG phenomena, including VEPs and mental tasks, have been explored, each with unique characteristics.

Conclusions:

• EEG biometrics is a developing field with considerable interest due to its security advantages.
• Further research is needed to overcome the challenge of feature extraction for robust and reliable EEG-based person identification.
• Exploring diverse EEG phenomena is crucial for advancing EEG biometrics.