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Related Concept Videos

Social Foundations of Self III: Self-Evaluation01:30

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Self-evaluation is the process by which individuals assess their abilities, behaviors, and characteristics based on feedback from others. Charles H. Cooley observed that a person’s self-perception is primarily influenced by how others see and judge them. He suggested that individuals form their identities based on their interpretations of others' reactions. As a result, social interactions play a crucial role in shaping self-esteem and personal identity. These external evaluations often...
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The Self-Evaluation Maintenance (SEM) model offers a psychological framework to understand how individuals’ self-esteem is influenced by the achievements of others, particularly those with whom they share close personal bonds. The SEM model operates when personal rather than social identity guides individuals. Central to this model is the notion that individuals have an inherent desire to preserve a favorable self-image, which is continuously shaped by interpersonal comparisons and...
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Updated: Nov 10, 2025

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Self-Relative Evaluation Framework for EEG-Based Biometric Systems.

Meriem Romaissa Boubakeur1, Guoyin Wang1

  • 1Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

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|April 3, 2021
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Summary
This summary is machine-generated.

This study introduces a novel self-relative evaluation framework for electroencephalogram (EEG) biometrics. The framework enhances identity information accuracy, particularly for new subject enrollment in EEG biometric systems.

Keywords:
biometricselectroencephalogram (EEG)frequency selectionidentity informationopen environmentperson identification

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Area of Science:

  • Neuroscience
  • Biometrics
  • Signal Processing

Background:

  • Electroencephalogram (EEG) signals are increasingly utilized as a biometric modality.
  • Processing EEG signals for identity extraction can lead to information loss, impacting system distinctiveness.
  • Existing EEG biometric systems face challenges with accuracy, especially when enrolling new subjects.

Purpose of the Study:

  • To propose a novel self-relative evaluation framework for EEG-based biometric systems.
  • To improve the selection of accurate identity information in open enrollment scenarios.
  • To address the distinctiveness issue caused by information loss during EEG signal processing.

Main Methods:

  • Development of a self-relative evaluation framework for EEG biometrics.
  • Experiments conducted on publicly available EEG datasets.
  • Utilized resting-state EEG data from 108 subjects with eyes closed.

Main Results:

  • The proposed framework effectively selects more accurate identity information.
  • The openness condition within the framework proved beneficial for accuracy.
  • Demonstrated improved distinctiveness in EEG-based biometric identification.

Conclusions:

  • The self-relative evaluation framework enhances the performance of EEG biometric systems.
  • Openness conditions are crucial for optimizing identity information selection in EEG biometrics.
  • This approach offers a promising solution for robust and accurate EEG-based identification.