Network weight adjustment in a fractional fourier transform based multi-channel brain computer interface for person authentication

Haque, Intisar Rizwan I; Khan, Muhammad Faisal; Saleem, Muhammad Mubasher; Rao, Naveed Iqbal

doi:10.1109/isspa.2012.6310682

Cited by 2 publications

(4 citation statements)

References 18 publications

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“…Based on the PSD obtained through squaring of the absolute value of Fourier-transformed data in each segment, the concavity of spectral distribution [107], variance of spectral power, and the nondominant region of the power spectrum [102] were calculated as EEG features for further recognition purpose. Fractional Fourier Transform (FRFT), seeking onedimensional time-frequency distribution, was used to extract fractional spectral coefficient values for each segment of normalized EEG data [139].…”

Section: Frequency Domainmentioning

confidence: 99%

“…The output function is based on the winner-takes-all principle. Other types of NNs were also adopted, such as the Elman Neural Network (ENN) [119,120] with the addition that the hidden layer outputs are delayed and fed back into the network; the Simplified Fuzzy ARTMAP 112:22 Q. Gui et al (SFA) [121,136], which consists of a fuzzy ART module for receiving the input features, a category layer with output classes, and an inter=ART module for learning the mapping between input features and corresponding output classes; and the Radial Basis Neural Network (RBNN) [139], which has one hidden radial basis layer and lower training time.…”

Section: Neural Networkmentioning

confidence: 99%

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A Survey on Brain Biometrics

et al. 2019

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Brainwaves, which reflect brain electrical activity and have been studied for a long time in the domain of cognitive neuroscience, have recently been proposed as a promising biometric approach due to their unique advantages of confidentiality, resistance to spoofing/circumvention, sensitivity to emotional and mental state, continuous nature, and cancelability. Recent research efforts have explored many possible ways of using brain biometrics and demonstrated that they are a promising candidate for more robust and secure personal identification and authentication. Although existing research on brain biometrics has obtained some intriguing insights, much work is still necessary to achieve a reliable ready-to-deploy brain biometric system. This article aims to provide a detailed survey of the current literature and outline the scientific work conducted on brain biometric systems. It provides an up-to-date review of state-of-the-art acquisition, collection, processing, and analysis of brainwave signals, publicly available databases, feature extraction and selection, and classifiers. Furthermore, it highlights some of the emerging open research problems for brain biometrics, including multimodality, security, permanence, and stability. CCS Concepts: • Security and privacy → Biometrics; • Computing methodologies → Machine learning approaches; • Applied computing → Psychology;

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Section: Frequency Domainmentioning

confidence: 99%

Section: Neural Networkmentioning

confidence: 99%

A Survey on Brain Biometrics

et al. 2019

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“…The identification was performed on 15 participants and the average classification varied from 79.7% to 89.5%. In [28], authors proposed an authentication paradigm based on five mental imagery tasks, namely relax, mathematical operation, letter composition, imaging of object rotation and number counting. Fractional spectral information was extracted and classified by means of an Exact Radial Basis (RBE) Neural Network.…”

Section: Eeg-based Cognitive Biometrics: Related Workmentioning

confidence: 99%

“…For instance, in one of the studies previously mentioned [28], authors stated that the results provided could be improved to nearly 100% by using more data and applying the voting rules for authentication. Authors in [30] proposed a multiple combination of classifiers and EEG features and fusing the best results to obtain a better performance.…”

Section: Eeg-based Cognitive Biometrics: Related Workmentioning

confidence: 99%

Authentication of Brain-Computer Interface Users in Network Applications

López-Gordo

Ron-Angevín

Pelayo

2015

Advances in Computational Intelligence

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Abstract. Cognitive biometrics aims to user authentication (or identification)by direct measure of electrophysiological signals as response to specific stimuli. In the literature, authentication paradigms for network applications are intended for healthy and independent users with complete control of their muscles. This excludes people with severe motor impairment, such as Brain-computer interface (BCI) users. Conversely, BCIs permit communication with users even in extreme impairment conditions, such as those suffering from locked-in syndrome or in advanced stage of Amyotrophic lateral sclerosis. The downside of BCIs is their very poor performance that, measured in terms of throughput and bit error rate, could lead to impracticable authentication. Specifically, current network applications require users to type long usernames and passwords formed with characters chosen from a large dataset. This forces long BCI sessions that users can not afford due to their heavy cognitive workload. In this paper we present some EEG-based authentication approaches and discuss some relevant aspects that a BCI-based authentication approach should consider for users with severe motor impairment.

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Network weight adjustment in a fractional fourier transform based multi-channel brain computer interface for person authentication

Cited by 2 publications

References 18 publications

A Survey on Brain Biometrics

A Survey on Brain Biometrics

Authentication of Brain-Computer Interface Users in Network Applications

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