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Distraction descriptor for brainprint authentication modelling using probability-based Incremental Fuzzy-Rough

Siaw-Hong Liew¹, Yun-Huoy Choo², Yin Fen Low³

¹Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak (UNIMAS), 94300, Kota Samarahan, Sarawak, Malaysia. shliew@unimas.my.

Brain Informatics

|August 5, 2023

Summary

This summary is machine-generated.

This study introduces a distraction descriptor for brainprint authentication, improving accuracy in real-world, uncontrolled environments using a novel probability-based incremental update strategy in Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN). The method effectively utilizes electroencephalogram (EEG) responses to ambient distractions.

Keywords:

Brainprint authentication Distraction descriptor Object variation Probability-based IncFRNN

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Area of Science:

Biometrics
Machine Learning
Neuroscience

Background:

Brainprint authentication models typically require controlled environments, limiting real-world applicability.
Ambient distractions in electroencephalogram (EEG) signals are often minimized, yet they offer unique user-specific responses.
Adapting authentication models to uncontrolled environments is crucial for practical deployment.

Purpose of the Study:

To design a distraction descriptor to refine granular knowledge incrementally.
To develop a probability-based incremental update strategy for Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN).
To enhance brainprint authentication models for uncontrolled environments.

Main Methods:

Proposed a distraction descriptor elicited through object variation.
Implemented a probability-based incremental update strategy within the IncFRNN technique.
Benchmarked the proposed strategy against ground truth and First-In-First-Out (FIFO) incremental update strategies in K-Nearest Neighbour (KNN).

Main Results:

The proposed distraction descriptor demonstrated equivalent discriminatory performance in both high distraction and quiet conditions.
The probability-based IncFRNN technique significantly outperformed KNN.
The method effectively utilizes unique EEG responses to ambient distractions for authentication.

Conclusions:

The study presents a more practical brainprint authentication model using a distraction descriptor and a probability-based IncFRNN strategy.
The proposed method enhances authentication in uncontrolled environments by leveraging EEG responses to distractions.
Future research should address intersession variability to further improve model robustness.