Person-identifying brainprints are stably embedded in EEG mindprints

Area of Science:

• Neuroscience
• Biometrics
• Machine Learning

Background:

• Electroencephalography (EEG) signals are used as biometric identifiers.
• The underlying factors contributing to the uniqueness of brain signals are not well understood.

Purpose of the Study:

• To investigate the nature and components of person-identifiable brain signals.
• To examine task invariance and temporal stability of these signals.
• To explore the distinguishability of individuals, including monozygotic twins.

Main Methods:

• Conducted a multi-task, multi-week EEG study with ten pairs of monozygotic (MZ) twins.
• Utilized machine-learning analyses to identify person-identifying EEG components.
• Compared EEG signal similarity within and between MZ twins.

Main Results:

• Discovered a stable, person-identifying EEG component termed "base signals" that are invariant across tasks and weeks.
• These "base signals" are more similar within MZ twins than between them.
• Individuals could be distinguished using EEG signals, particularly those from later-developed brain regions.

Conclusions:

• Identified "base signals" in EEG as structural brainprints rather than functional mindprints due to their stability and task invariance.
• Demonstrated the potential of EEG-based "base signals" for distinguishing individuals, even MZ twins.
• Highlighted practical implications for privacy protection and brain-computer interface applications.