Contrast between Spectral and Connectivity Features for Electroencephalography based Authentication

Han, Chul–Hi; Kim, S. K.; Yoon, Heenam; Lee, W. K.; Park, C. S.; Kim, K. K.; Park, K. S.

doi:10.1007/978-3-319-19387-8_298

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…The selection of the mentioned frequency band is because this range consisting of dominant brain activities that able to recognize person disregard of brain tasks [15], [16]. The frequency band will later be tested in a combined and separated manner to identify the effective region for better efficiency [14].…”

Section: Features Extractionmentioning

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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Section: Features Extractionmentioning

confidence: 99%

Task Sensitivity in Continuous Electroencephalogram Person Authentication

Wong¹,

Choo²,

Kamilah³

2020

IJACSA

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“…Indeed, some unique individual-specific features can be extracted from EEG signals due to genetic and environmental factors [ 18 ]. Many methods have been used in feature extraction, namely the autoregressive model (AR) [ 19 ], power spectral density (PSD) [ 20 ], wavelet transform [ 21 ], coherence features (COH) [ 22 ], shannon entropy [ 23 ] and common spatial patterns [ 24 ], among others. The AR model was first used in the earlier work of Poulos et al.…”

Section: Introductionmentioning

confidence: 99%

Self-Relative Evaluation Framework for EEG-Based Biometric Systems

Boubakeur

Wang

2021

Sensors

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In recent years, electroencephalogram (EEG) signals have been used as a biometric modality, and EEG-based biometric systems have received increasing attention. However, due to the sensitive nature of EEG signals, the extraction of identity information through processing techniques may lead to some loss in the extracted identity information. This may impact the distinctiveness between subjects in the system. In this context, we propose a new self-relative evaluation framework for EEG-based biometric systems. The proposed framework aims at selecting a more accurate identity information when the biometric system is open to the enrollment of novel subjects. The experiments were conducted on publicly available EEG datasets collected from 108 subjects in a resting state with closed eyes. The results show that the openness condition is useful for selecting more accurate identity information.

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“…There are various applications based on electroencephalogram (EEG) signals, such as brain computer interface (BCI), brain mapping, sleep research, clinical diagnosis, and authentication [1,2,3,4,5,6]. Multichannel measurement of EEG signals is required for such applications.…”

Section: Introductionmentioning

confidence: 99%

An Instant Donning Multi-Channel EEG Headset (with Comb-Shaped Dry Electrodes) and BCI Applications

Kim

Lee

Han

et al. 2019

Sensors

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We developed a new type of electroencephalogram (EEG) headset system with comb-shaped electrodes that enables the wearer to quickly don and utilize it in daily life. Two models that can measure EEG signals using up to eight channels have been implemented. The electrodes implemented in the headsets are similar to a comb and are placed quickly by wiping the hair (as done with a comb) using the headset. To verify this headset system, donning time was measured and three brain computer interface (BCI) application experiments were conducted. Alpha rhythm-based, steady-state visual evoked potential (SSVEP)-based, and auditory steady state response (ASSR)-based BCI systems were adopted for the validation experiments. Four subjects participated and ten trials were repeated in the donning experiment. The results of the validation experiments show that reliable EEG signal measurement is possible immediately after donning the headsets without any preparation. It took approximately 10 s for healthy subjects to don the headsets, including an earclip with reference and ground electrodes. The results of alpha rhythm-based BCI showed 100% accuracy. Furthermore, the results of SSVEP-based and ASSR-based BCI experiments indicate that performance is sufficient for BCI applications; 95.7% and 76.0% accuracies were obtained, respectively. The results of BCI paradigm experiments indicate that the new headset type is feasible for various BCI applications.

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Contrast between Spectral and Connectivity Features for Electroencephalography based Authentication

Cited by 4 publications

References 4 publications

Task Sensitivity in Continuous Electroencephalogram Person Authentication

Task Sensitivity in Continuous Electroencephalogram Person Authentication

Self-Relative Evaluation Framework for EEG-Based Biometric Systems

An Instant Donning Multi-Channel EEG Headset (with Comb-Shaped Dry Electrodes) and BCI Applications

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