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Association Areas of the Cortex01:21

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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
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Related Experiment Video

Updated: Jul 20, 2025

How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study
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How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

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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
PubMed
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 authenticationDistraction descriptorObject variationProbability-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.