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Related Experiment Video

Updated: Jul 16, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.

Marissa L de Ataide1, Narayan Vetrekar1, Krishna Patel1

  • 1School of Physical and Applied Sciences, Goa University, Taleigao 403206, Goa, India.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

Electroencephalography (EEG) offers unique, spoof-resistant biometric authentication. This survey details EEG systems, challenges, and future research for robust brainwave-based security.

Keywords:
EEG devicesacquisition protocolauthenticationbiometricbrain signalschallengesclassificationdatabasefeature extractionidentificationpreprocessingverification

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Area of Science:

  • Neuroscience
  • Biometrics
  • Computer Science

Background:

  • Electroencephalography (EEG) is a promising biometric modality due to unique brainwave patterns.
  • EEG offers resistance to spoofing attacks, enhancing user authentication security.
  • Existing research on EEG-based authentication progress requires comprehensive evaluation.

Purpose of the Study:

  • To provide an in-depth survey of EEG-based user authentication systems.
  • To review current methodologies, challenges, and future directions in the field.
  • To serve as a foundational reference for researchers in EEG biometrics.

Main Methods:

  • Overview of brain structure and EEG signal acquisition principles.
  • Review of EEG databases, preprocessing techniques, feature extraction, and classification algorithms.
  • Identification of challenges like signal variability and the need for robust algorithms.

Main Results:

  • Detailed examination of EEG signal processing for biometric authentication.
  • Analysis of various feature extraction and classification strategies.
  • Identification of key challenges impacting algorithm stability and robustness.

Conclusions:

  • EEG biometrics presents a secure authentication method with significant potential.
  • Addressing signal variability and inter-subject differences is crucial for robust systems.
  • This survey provides a roadmap for future advancements in EEG-based biometric systems.