Spatial and Spectral Fingerprint in the Brain: Speaker Identification from Single Trial MEG Signals

@inproceedings{Dash2019SpatialAS,
  title={Spatial and Spectral Fingerprint in the Brain: Speaker Identification from Single Trial MEG Signals},
  author={Debadatta Dash and Paul Ferrari and Jun Wang},
  booktitle={INTERSPEECH},
  year={2019}
}
Brain activity signals are unique subject-specific biological features that can not be forged or stolen. Recognizing this inherent trait, brain waves are recently being acknowledged as a far more secure, sensitive, and confidential biometric approach for user identification. Yet, current electroencephalography (EEG) based biometric systems are still in infancy considering their requirement of a large number of sensors and lower recognition performance compared to present biometric modalities… 

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References

SHOWING 1-10 OF 35 REFERENCES

Towards EEG biometrics: pattern matching approaches for user identification

Experimental results show that, the Oz channel provides the best identification accuracy for both ED and DTW methods, and the stimuli of illegal strings and words seem to trigger more distinguishable brain responses.

EEG Based Biometric Framework for Automatic Identity Verification

This study indicates the enormous potential of the EEG biometrics, especially due to its robustness against fraud, and proposes several advances in the feature extraction and classification stages.

Human Brain Distinctiveness Based on EEG Spectral Coherence Connectivity

A novel approach that fuses spectral coherence-based connectivity between different brain regions as a possibly viable biometric feature is proposed and it is suggested that the functional connectivity patterns represent effective features for improving EEG-based biometric systems.

Brain waves for automatic biometric-based user recognition

The issues, which represent an obstacle toward the deployment of biometric systems based on the analysis of brain activity in real life applications are speculated on and a critical and comprehensive review of state-of-the-art methods for electroencephalogram-based automatic user recognition is provided.

Subject identification from electroencephalogram (EEG) signals during imagined speech

  • Katharine BrighamB. Kumar
  • Computer Science
    2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS)
  • 2010
The proposed approach was tested on a publicly available database consisting of EEG signals corresponding to Visual Evoked Potentials to test the applicability of the proposed method on a larger number of subjects, and it was able to classify 120 subjects with 98.96% accuracy.

Person Authentication Using Brainwaves (EEG) and Maximum A Posteriori Model Adaptation

  • S. MarcelJ. Millán
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2007
The use of brain activity for person authentication is investigated and a statistical framework based on Gaussian mixture models and maximum a posteriori model adaptation, successfully applied to speaker and face authentication, is proposed, which can deal with only one training session.

Feature selection and channel optimization for biometric identification based on visual evoked potentials

Results in this study revealed the feasibility of VEPs based EEG to be used for biometric identification and the proposed optimization algorithm was shown to have the ability to effectively improve the identification accuracy as well as simplifying the system.

Fisher linear discriminant based person identification using visual evoked potentials

The feasibility of visual evoked potential (VEP) in the gamma band of EEG signal, as a physiological trait, is studied, and used to identify individuals in a group of 20 people.

Novel HHT-Based Features for Biometric Identification Using EEG Signals

  • Su YangF. Deravi
  • Computer Science
    2014 22nd International Conference on Pattern Recognition
  • 2014
In this paper we present a novel approach for biometric identification using electroencephalogram (EEG) signals based on features extracted with the Hilbert-Huang Transform (HHT). The instantaneous

Wavelet-Based EEG Preprocessing for Biometric Applications

  • Su YangF. Deravi
  • Computer Science
    2013 Fourth International Conference on Emerging Security Technologies
  • 2013
This paper compares the effectiveness of different wavelet-based noise removal methods and proposes an EEG-based biometric identification system which combines two such de-noising methods to enhance the signal preprocessing stage.