Brain signal׳s low-frequency fits the continuous authentication

Matsuyama, Yasuo; Shozawa, Michitaro; Yokote, Ryota

doi:10.1016/j.neucom.2014.08.084

Cited by 22 publications

(16 citation statements)

References 8 publications

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“…Study in [6] records continuous EEG from only Fp1 electrode in the simulated driving environment and achieved the best of 27% EER, the recording lasts for three (3) minutes per trial and collected twice a day for five (5) days from thirty (30) subjects. Apart from EEG, a study in [7] records continuous brainwaves in near-infrared spectroscopy (NIRS) for 60s while the user was doing typing tasks. Only a single probe placed on the subject forehead was used to minimal interruption, where this study able to obtain 0.40% EER.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, the time-series EEG signals from one electrode contribute to 240,000 data points (8 mins * 60s * 500Hz). Next, the data was segmented equally in 10s epoch without overlapping [7], for each subject and each electrode. Therefore, each user possesses a total of 2k=48 data trials with respective subject labeled, where all electrodes are concatenated in dimension.…”

Section: B Signal Pre-processingmentioning

confidence: 99%

“…: resting, visual stimulus, mental imaginary, and etc.). Although experiments involved multimodal stimulus that fulfilled unconscious set-up has introduced in several studies to improve the practicality for brainwave continuous authentication [6], [7]. However, these experiments involve only a consistent EEG stimulus set throughout the whole recording session, where the same set of EEG cognitive tasks is possible to occur in every data trial for both data training and testing sets.…”

Section: Introductionmentioning

confidence: 99%

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Task Sensitivity in Continuous Electroencephalogram Person Authentication

Wong¹,

Choo²,

Kamilah³

2020

IJACSA

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This research investigates on the task sensitivity in multimodal stimulation task for continuous person authentication using the electroencephalogram (EEG) signals. Pattern analysis aims to train from historical examples for prediction on the unseen data. However, data trials in EEG stimulation consists of inseparable cognitive information that is difficult to ensure that the testing trials contain the cognitive information matching to the training data. Since the EEG signals are unique across individuals, we assume that multimodal stimulation task in EEG analysis is not sensitive in train-test data trials control. Data trial inconsistency during training and testing can still be used as biometrics to authenticate a person. The EEG signals were collected using the 10-20 systems from 20 healthy subjects. During data acquisition, subjects were asked to operate a computer and perform various computer-related tasks (e.g.: mouse click, mouse scrolling, keyboard typing, browsing, reading, video watching, music listening, playing computer games, and etc.) as their preferences, without interruption. Features extracted from Welch's estimated Power Spectral Density in different frequency bands were tested. The designed authentication approach computed intra-and inter-personal variability using Mahalanobis distance to authenticate subject. The proposed EEG continuous authentication approach has succeeded. Data collected from multimodal stimulus disregard of task sensitivity able to authenticate subject, where the highest verification performance shown in the low-Beta frequency band. Evidence found that effective frequency region on the middle band was anticipated due to the data collected was based on subject voluntary actions. Future research will focus on the effect of subject voluntary and involuntary actions on the effective frequency region.

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Section: Related Workmentioning

confidence: 99%

Section: B Signal Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Task Sensitivity in Continuous Electroencephalogram Person Authentication

Wong¹,

Choo²,

Kamilah³

2020

IJACSA

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“…Experiments on Continuous-Symbol Time Series 1) Data Preparation: We collected a two-dimensional vector time series of oxyhemoglobin and deoxyhemoglobin changes, using NIRS (near infrared spectroscopy). NIRS brain signals are of practical use in a variety of applications, including humanoid control [29], [30] and user authentication applications [31]. In practice, repeated measurements of continuous-symbol biological signals rarely give a set of {y m t } T t=1 that are independent and identically distributed in m. Therefore, we chose M = 2 and N = 108 for NIRS vector brain signals.…”

Section: ) Experiments On the Left-to-right Modelmentioning

confidence: 99%

The Alpha-HMM Estimation Algorithm: Prior Cycle Guides Fast Paths

Matsuyama

2017

IEEE Trans. Signal Process.

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The estimation of generative structures for sequences is becoming increasingly important for preventing such data sources from becoming a flood of disorganized information. Obtaining Hidden Markov models (HMMs) has been a central method for structuring such data. However, users have been aware of the slow speed of this algorithm. In this study, we devise generalized and fast estimation methods for HMMs by employing a geometric information measure that is associated with a function called the alpha-logarithm. Using the alphalogarithmic likelihood ratio, we exploit prior iterations to guide rapid convergence. The parameter alpha is used to adjust the utilization of previous information. A fixed-point approach using a causal shift and a series expansion is responsible for this gain. For software implementations, we present probability scaling to avoid underflow, where we generalize flaw corrections to the de facto standard. For the update mechanism, we begin with a method called shotgun surrogates, in relation to the parameter alpha. Then, we obtain a dynamic version that employs the controlling and undoing of alpha. Experiments on biological sequences and brain signals for practical state models demonstrate that a significant speedup is achieved compared to the Baum-Welch method. The effects of restricting the state models are also reported.Index Terms-Alpha-hidden Markov model estimation, message passing, shotgun surrogates, dynamic surrogate, convergence speedup.1053-587X (c)

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“…It might be said to be the origin of every action we take. Brainwaves are the external signals of a functioning brain, and thus provide a glimpse into what we are thinking and doing [7]. A good example is that, when an individual closes his/her eyes, we can observe a slightly shivering alpha wave embedded in the brainwaves [8].…”

Section: Introductionmentioning

confidence: 99%

Authentication with What You See and Remember in the Internet of Things

Chiu

Fan

et al. 2018

Symmetry

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The Internet-of-Things (IoT) is an emerging paradigm seamlessly integrating a great number of smart objects ubiquitously connected to the Internet. With the rise in interest in the IoT, industry and academia have introduced a variety of authentication technologies to deal with security challenges. Authentication in IoT involves not only shifting intelligent access control down to the end smart objects, but also user identification and verification. In this paper, we build an authentication system based on brainwave reactions to a chain of events. Brainwaves, as external signals of a functioning brain, provide a glimpse into how we think and react. However, seen another way, we could reasonably expect that a given action or event could be linked back to its corresponding brainwave reaction. Recently, commercial products in the form of wearable brainwave headsets have appeared on the market, opening up the possibility of exploiting brainwaves for various purposes and making this more feasible. In the proposed system, we use a commercially available brainwave headset to collect brainwave data from participants for use in the proposed authentication system. After the brainwave data collection process, we apply a machine learning-based approach to extract features from brainwaves to serve as authentication tokens in the system and support the authentication system itself.

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Brain signal׳s low-frequency fits the continuous authentication

Cited by 22 publications

References 8 publications

Task Sensitivity in Continuous Electroencephalogram Person Authentication

Task Sensitivity in Continuous Electroencephalogram Person Authentication

The Alpha-HMM Estimation Algorithm: Prior Cycle Guides Fast Paths

Authentication with What You See and Remember in the Internet of Things

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