Exploiting the P300 paradigm for cognitive biometrics

Gupta, Cota Navin; Palaniappan, Ramaswamy; Raveendran, P.

doi:10.1504/ijcb.2012.046513

Cited by 29 publications

(6 citation statements)

References 23 publications

(24 reference statements)

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“…It can be further divided into steady state evoked potential (SSEP) and event-related potential (ERP) [124]. EP components are labeled either exogenous or endogenous [125]. Exogenous components are influenced by physical attributes of stimuli such as intensity, modality, and presentation rate.…”

Section: Evoked Potential or Evoked Response (Ep)mentioning

confidence: 99%

Brain computer interfacing: Applications and challenges

Abdulkader

Atia

Mostafa

2015

Egyptian Informatics Journal

449

247

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Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabilitation for serious injuries. Mind reading and remote communication have their unique fingerprint in numerous fields such as educational, self-regulation, production, marketing, security as well as games and entertainment. It creates a mutual understanding between users and the surrounding systems. This paper shows the application areas that could benefit from brain waves in facilitating or achieving their goals. We also discuss major usability and technical challenges that face brain signals utilization in various components of BCI system. Different solutions that aim to limit and decrease their effects have also been reviewed.Ó 2015 Production and hosting by Elsevier B.V. on behalf

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Section: Evoked Potential or Evoked Response (Ep)mentioning

confidence: 99%

Brain computer interfacing: Applications and challenges

Abdulkader

Atia

Mostafa

2015

Egyptian Informatics Journal

449

247

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“…Only a handful of studies have been done where EEG and ECG signals have been used as a source of biometric information. Few researchers who have worked in the area of EEG have reported a success rate of 70-90% [8]- [14]. Similarly, 90-96% success rate has been reported when ECG has been used as a source of biometric information [6] and [15]- [17].…”

Section: Introductionmentioning

confidence: 99%

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

Mishra¹,

Singla²

2013

Measurement Science Review

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In the modern world of automation, biological signals, especially Electroencephalogram (EEG) and Electrocardiogram (ECG), are gaining wide attention as a source of biometric information. Earlier studies have shown that EEG and ECG show versatility with individuals and every individual has distinct EEG and ECG spectrum. EEG (which can be recorded from the scalp due to the effect of millions of neurons) may contain noise signals such as eye blink, eye movement, muscular movement, line noise, etc. Similarly, ECG may contain artifact like line noise, tremor artifacts, baseline wandering, etc. These noise signals are required to be separated from the EEG and ECG signals to obtain the accurate results. This paper proposes a technique for the removal of eye blink artifact from EEG and ECG signal using fixed point or FastICA algorithm of Independent Component Analysis (ICA). For validation, FastICA algorithm has been applied to synthetic signal prepared by adding random noise to the Electrocardiogram (ECG) signal. FastICA algorithm separates the signal into two independent components, i.e. ECG pure and artifact signal. Similarly, the same algorithm has been applied to remove the artifacts (Electrooculogram or eye blink) from the EEG signal.

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“…Distance is set approximately one meter. Based on the studies in [5]- [7] found that the optimal parameter for the subject to reduce visual fatigue or visual discomfort. For each trial, therefore, we set the duty cycle (D) is 0.7, flickering rate (F) is fixed at 7 Hz, light intensity (I) is 10%, and chromatic color (C) is green.…”

Section: Methodsmentioning

confidence: 99%

Effects of visual flickering stimuli for event-related potential recording

Dangruan

Jatadhammakorn

Luxsameepicheat

et al. 2016

2016 8th International Conference on Knowledge and Smart Technology (KST)

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Brain-Computer Interface (BCI) enables peoples with disabilities communicate the outer world by using electroencephalogram (EEG) signal. One of the most efficient paradigms, namely, the event-related potential (ERP) based BCI. As a result, the visual stimulation is being recognized to be a most efficient stimulus for a multiple BCI system. In this paper, therefore, we offline investigated the effects of visual flickering stimuli for ERP recording in terms of optimizing the parameter of visual simulation, which presents a time-locked EEG classification. Different experimental conditions were compared to achieve the best performance. The best experiment showed that the classification accuracy rate of 80% approximately when the stimulus epoch interval was 500 ms across 3 trials. It can be useful for implementing real-time multiple ERP based BCI system.

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Exploiting the P300 paradigm for cognitive biometrics

Cited by 29 publications

References 23 publications

Brain computer interfacing: Applications and challenges

Brain computer interfacing: Applications and challenges

Artifact Removal from Biosignal using Fixed Point ICA Algorithm for Pre-processing in Biometric Recognition

Effects of visual flickering stimuli for event-related potential recording

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