Overview of Acquisition Protocol in EEG Based Recognition System

Yap, Hui Yen; Choo, Yun-Huoy; Khoh, Wee How

doi:10.1007/978-3-319-70772-3_12

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…The result is then translated into a command and the system is instructed to complete the intended task [1]. Recently, the research of BCI has been extended further to cover several applications, including authentication and security [4].…”

Section: Related Workmentioning

confidence: 99%

“…Brain activity can be obtained through EEG recordings using specifically designed protocols, including the resting state, motor imaginary, non-motor imaginary and stimulation protocol [4]. The resting-state protocol is easy to operate as it only requires users to rest for a few minutes in either eyes-closed (EC) or eyes-open (EO) state, while the EEG data are recorded.…”

Section: Introductionmentioning

confidence: 99%

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Person authentication based on eye-closed and visual stimulation using EEG signals

et al. 2021

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The study of Electroencephalogram (EEG)-based biometric has gained the attention of researchers due to the neurons’ unique electrical activity representation of an individual. However, the practical application of EEG-based biometrics is not currently widespread and there are some challenges to its implementation. Nowadays, the evaluation of a biometric system is user driven. Usability is one of the concerning issues that determine the success of the system. The basic elements of the usability of a biometric system are effectiveness, efficiency and user satisfaction. Apart from the mandatory consideration of the biometric system’s performance, users also need an easy-to-use and easy-to-learn authentication system. Thus, to satisfy these user requirements, this paper proposes a reasonable acquisition period and employs a consumer-grade EEG device to authenticate an individual to identify the performances of two acquisition protocols: eyes-closed (EC) and visual stimulation. A self-collected database of eight subjects was utilized in the analysis. The recording process was divided into two sessions, which were the morning and afternoon sessions. In each session, the subject was requested to perform two different tasks: EC and visual stimulation. The pairwise correlation of the preprocessed EEG signals of each electrode channel was determined and a feature vector was formed. Support vector machine (SVM) was then used for classification purposes. In the performance analysis, promising results were obtained, where EC protocol achieved an accuracy performance of 83.70–96.42% while visual stimulation protocol attained an accuracy performance of 87.64–99.06%. These results have demonstrated the feasibility and reliability of our acquisition protocols with consumer-grade EEG devices.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Person authentication based on eye-closed and visual stimulation using EEG signals

et al. 2021

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“…Typically, the EEG signal acquisition protocol can be grouped into three (3) categories based on the review from [8]- [10], which are: resting-state/relaxation, event stimulation, and mental imaginary. The resting-state protocol requires subject to sit with relaxing in eye open or eye closing condition, EEG signal will be acquired during this human quiescent state.…”

Section: Eeg Protocolsmentioning

confidence: 99%

“…However, to reduce the capture of noise, EEG signals www.ijacsa.thesai.org collection begins while the indicator for all electrodes appears green and prolonged for at least five (5) seconds. The experiment runs for one (1) time only for each subject and the total duration for tasked recording lasted for ten (10) minutes. The experiment was done in a quiet and enclosed room dedicated to the EEG experiment.…”

Section: A Experiments Paradigm Design and Data Collectionmentioning

confidence: 99%

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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Simultaneous EEG Analysis and Feature Extraction Selection Based on Unsupervised Learning

Almarri

Huang

2018

Brain Informatics

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Overview of Acquisition Protocol in EEG Based Recognition System

Cited by 8 publications

References 20 publications

Person authentication based on eye-closed and visual stimulation using EEG signals

Person authentication based on eye-closed and visual stimulation using EEG signals

Task Sensitivity in Continuous Electroencephalogram Person Authentication

Simultaneous EEG Analysis and Feature Extraction Selection Based on Unsupervised Learning

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