Person identification based on parametric processing of the EEG

Poulos, Marios; Rangoussi, Maria; Chrissikopoulos, Vassilios; Evangelou, A.

doi:10.1109/icecs.1999.812278

Cited by 140 publications

(95 citation statements)

References 13 publications

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“…An earlier study that investigates the relationship between the EEG and the genetic uniqueness of individuals has been conducted by Poulos et al [12]. They have employed autoregressive (AR) model to extract the parameters from the EEG and described the alpha rhythms.…”

Section: Relatedmentioning

confidence: 99%

“…The studies suggest that the brain electrical activity of an individual is unique and EEG can be used as a new biometric for people identity verification [12][13][14][15][16][17]. Although, the data acquisition of the EEG is somewhat cumbersome.…”

Section: Supporting Factorsmentioning

confidence: 99%

“…Bioelectrical signals especially, the ECG and the EEG are emerging biometric identities [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Unlike anatomical biometric identities that have two-dimensional data representation, the ECG or EEG is physiologically low-frequency signals that have one-dimensional data representation.…”

Section: Characteristics Of Bioelectrical Signals As Biometricsmentioning

confidence: 99%

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Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Singh

Ray

2012

ISRN Signal Processing

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This paper presents the effectiveness of bioelectrical signals such as the electrocardiogram (ECG) and the electroencephalogram (EEG) for biometric applications. Studies show that the impulses of cardiac rhythm and electrical activity of the brain recorded in ECG and EEG, respectively; have unique features among individuals, therefore they can be suggested to be used as biometrics for identity verification. The favourable characteristics to use the ECG or EEG signals as biometric include universality, measurability, uniqueness and robustness. In addition, they have the inherent feature of vitality that signifies the life signs offering a strong protection against spoof attacks. Unlike conventional biometrics, the ECG or EEG is highly confidential and secure to an individual which is difficult to be forged. We present a review of methods used for the ECG and EEG as biometrics for individual authentication and compare their performance on the datasets and test conditions they have used. We illustrate the challenges involved in using the ECG or EEG as biometric primarily due to the presence of drastic acquisition variations and the lack of standardization of signal features. In order to determine the large-scale performance, individuality of the ECG or EEG is another challenge that remains to be addressed.

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Section: Relatedmentioning

confidence: 99%

Section: Supporting Factorsmentioning

confidence: 99%

Section: Characteristics Of Bioelectrical Signals As Biometricsmentioning

confidence: 99%

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Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Singh

Ray

2012

ISRN Signal Processing

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“…Originally AR model was developed as useful tool to describe and analyze 1-D discrete time signals in [41,42]. 2-D applications of the AR model were first proposed by Kashyap and Chellpa [43] who used the model for shape storage, transmission and reconstruction.…”

Section: Time Series Based Ar Modelingmentioning

confidence: 99%

Personal Verification Using Two Level Fusion Schemes Based on Ear and Iris Biometrics

Khursheed¹

2016

IJCDS

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Increased threats to conventional personnel verification methods, have given rise to verification methods based on biometrics. This paper presents a novel approach for personal verification based on fusion of two biometric modalities: Ear and Iris. Fusion has been done in two levels. In level one right iris and left iris features have been fused using texture based Grey Level Concurrence Matrix (GLCM) and in level two these features have been fused with AR coefficients taken from ear substructure. It has been found that this two level fusion of features improves the recognition rate of a person to the extent of 100%. The biometric modalities of ear and iris have been chosen on the basis of their desirable properties like uniqueness, universality, permanency and acceptability.

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“…ERD/ERS pattern was also identified as a possible stable biometric marker, in a BCI context (Pfurtscheller and Neuper 2006). Other classification frameworks explored include autoregressive (AR) features with different classifiers (Poulos et al, 1999). However, most studies until now have failed to investigate the long-term stability of the features.…”

Section: Introductionmentioning

confidence: 99%

Exploiting the P300 paradigm for cognitive biometrics

Gupta¹,

Palaniappan²,

Raveendran³

2012

IJCB

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Automatic identification of a person's individuality is an important issue today. Brain Computer Interfaces (BCI) which uses EEG as a modality is a promising area for cognitive biometrics. A BCI system could be used to recognise a sequence (say letters, colours or images) by the user. This sequence could form a 'BrainWord', which could be used for authentication in a multimodal environment with other technologies for high security applications. In this work, we studied several variations of the well-known P300 BCI paradigm. The influence of irrelevant stimuli during a task was studied by considering the popular Rapid Serial Visual Paradigm (RSVP). The variation in spatial locations of the presentation stimuli during a task was studied, by designing a Spatially Varying Paradigm. Comparison of classification accuracies and bit rates for eight participants from a BCI perspective, highlights that RSVP paradigm could be exploited effectively for biometrics.

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Person identification based on parametric processing of the EEG

Cited by 140 publications

References 13 publications

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Personal Verification Using Two Level Fusion Schemes Based on Ear and Iris Biometrics

Exploiting the P300 paradigm for cognitive biometrics

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