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نتیجه جستجو - EEG

تعداد مقالات یافته شده: 58
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1 Person-identification using familiar-name auditory evoked potentials from frontal EEG electrodes
شناسایی فرد با استفاده از پتانسیل نام-آشنا شنوایی الکترودهای EEG جلو برانگیخته-2021
Electroencephalograph (EEG) based biometric identification has recently gained increased attention of re- searchers. However, state-of-the-art EEG-based biometric identification techniques use large number of EEG electrodes, which poses user inconvenience and consumes longer preparation time for practical applications. This work proposes a novel EEG-based biometric identification technique using auditory evoked potentials (AEPs) acquired from two EEG electrodes. The proposed method employs single-trial familiar-name AEPs extracted from the frontal electrodes Fp1 and F7, which facilitates faster and user-convenient data acquisition. The EEG signals recorded from twenty healthy individuals during four experiment trials are used in this study. Different com- binations of well-known neural network architectures are used for feature extraction and classification. The cascaded combinations of 1D-convolutional neural networks (1D-CNN) with long short-term memory (LSTM) and with gated recurrent unit (GRU) networks gave the person identification accuracies above 99 %. 1D-convolutional, LSTM network achieves the highest person identification accuracy of 99.53 % and a half total error rate (HTER) of 0.24 % using AEP signals from the two frontal electrodes. With the AEP signals from the single electrode Fp1, the same network achieves a person identification accuracy of 96.93 %. The use of familiar-name AEPs from frontal EEG electrodes that facilitates user convenient data acquisition with shorter preparation time is the novelty of this work.
Keywords: Auditory evoked potential | Biometrics | Deep learning | Electroencephalogram | Familiar-name | Person identification
مقاله انگلیسی
2 A comprehensive survey on the biometric systems based on physiological and behavioural characteristics
مرور جامع سیستم های بیومتریک بر اساس ویژگی های فیزیولوژیکی و رفتاری-2021
With the fast increasing of the electronic crimes and their related issues, deploying a reliable user authentication system became a significant task for both of access control and securing user’s private data. Human biometric characteristics such as voice, finger, iris scanning, face, signature and other features provide a dependable security level for both of the personal and the public use. Many biometric authentication systems have been approached for long time. Due to the uniqueness of human biometrics witch played a master role in degrading imposters’ attacks. Such authentication models have overcome other traditional security methods like passwords and PIN. This paper aims to briefly address the psychological biometric authentication techniques and a brief summary to the advantages, disadvantages of each method. Main contribution it found that used EEG signals, as biometrics is the best technique compare to with five other techniques.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Nanoelectronics, Nanophotonics, Nanomaterials, Nanobioscience & Nanotechnology.
Keywords: Biometrics | Physiological | Behavioral | Identification | Techniques
مقاله انگلیسی
3 EBAPy: A Python framework for analyzing the factors that have an influence in the performance of EEG-based applications
EBAPy: یک چارچوب پایتون برای تجزیه و تحلیل عوامل موثر بر عملکرد برنامه های مبتنی بر EEG-2021
EBAPy is an easy-to-use Python framework intended to help in the development of EEG-based applications. It allows performing an in-depth analysis of factors that influence the performance of the system and its computational cost. These factors include recording time, decomposition level of Discrete Wavelet Transform, and classification algorithm. The ease-of-use and flexibility of the presented framework have allowed reducing the development time and evaluating new ideas in developing biometric systems using EEGs. Furthermore, different applications that classify EEG signals can use EBAPy because of the generality of its functions. These new applications will impact human–computer interaction in the near future.Code metadataCurrent code version v1.1Permanent link to code/repository used for this code version https://github.com/SoftwareImpacts/SIMPAC-2021-2Permanent link to Reproducible Capsule https://codeocean.com/capsule/4497139/tree/v1Legal Code License MITCode versioning system used gitSoftware code languages, tools, and services used Python Compilation requirements, operating environments & dependencies If available Link to developer documentation/manualSupport email for questions dustin.carrion@gmail.com
Keywords: EEG-based applications | Recording time | Discrete wavelet transform
مقاله انگلیسی
4 A relational account of visual short-term memory (VSTM)
یک گزارش ارتباطی از حافظه کوتاه مدت بصری (VSTM)-2021
Visual short-term memory (VSTM) is an important resource that allows temporarily storing visual information. Current theories posit that elementary features (e.g., red, green) are encoded and stored independently of each other in VSTM. However, they have difficulty explaining the similarity effect, that similar items can be remembered better than dissimilar items. In Experiment 1, we tested (N ¼ 20) whether the similarity effect may be due to storing items in a context-dependent manner in VSTM (e.g., as the reddest/yellowest item). In line with a relational account of VSTM, we found that the similarity effect is not due to feature similarity, but to an enhanced sensitivity for detecting changes when the relative colour of a to-be-memorised item changes (e.g., from reddest to not-reddest item; than when an item underwent the same change but retained its relative colour; e.g., still reddest). Experiment 2 (N ¼ 20) showed that VSTM load, as indexed by the CDA amplitude in the EEG, was smaller when the colours were ordered so that they all had the same relationship than when the same colours were out-of-order, requiring encoding different relative colours. With this, we report two new effects in VSTM e a relational detection advantage that describes an enhanced sensitivity to relative changes in change detection, and a relational CDA effect, which reflects that VSTM load, as indexed by the CDA, scales with the number of (different) relative features between the memory items. These findings support a relational account of VSTM and question the view that VSTM stores features such as colours independently of each other.
keywords: حافظه کوتاه مدت بصری (VSTM) | حافظه کاری ویژوال (VWM) | حساب ارتباطی | اثر شباهت | CDA برای دیدن | Visual short-term memory (VSTM) | Visual working memory (VWM) | Relational account | Similarity effect | CDA in EEG
مقاله انگلیسی
5 Graph Variational Auto-Encoder for Deriving EEG-based Graph Embedding
رمزگذار خودکار گرافیکی برای استخراج نمودار مبتنی بر EEG-2021
Graph embedding is an effective method for deriving low-dimensional representations of graph data. The power of graph deep learning methods to characterize electroencephalogram (EEG) graph embedding is still in question. We designed a novel graph variational auto-encoder (GVAE) method to extract nodal features of brain functional connections. A new decoder model for the GVAEs network is proposed, which considers the node neighborhood of the reconstructed adjacency matrix. The GVAE is applied and tested on 3 biometric databases which contain 64 to 9 channels’ EEG recordings. For all datasets, promising results with more than 95% accuracy and consider- ably low computational cost are achieved compared to state-of-the-art user identification methods. The proposed GVAE is robust to a limited number of nodes and stable to users’ task performance. Moreover, we developed a Abstract Graph embedding is an effective method for deriving low-dimensional representations of graph data. The power of graph deep learning methods to characterize electroencephalogram (EEG) graph embedding is still in ques- tion. We designed a novel graph variational auto-encoder (GVAE) method to extract nodal features of brain functional connections. A new decoder model for the GVAEs network is proposed, which considers the node neighborhood of the reconstructed adjacency matrix. The GVAE is applied and tested on 3 biometric databases which contain 64 to 9 channels’ EEG recordings. For all datasets, promising results with more than 95% accuracy and consider- ably low computational cost are achieved compared to state-of-the-art user identification methods. The proposed GVAE is robust to a limited number of nodes and stable to users’ task performance. Moreover, we developed atraditional variational auto-encoder to demonstrate that more accurate features can be obtained when observing EEG-based brain connectivity from a graph perspective. Email addresses: tina.behrouzi@mail.utoronto.ca (Tina Behrouzi),dimitris@comm.utoronto.ca (Dimitrios Hatzinakos)Preprint submitted to Pattern Recognition July 20, 2021
Keywords: Biometrics | Functional connectivity | Electroencephalogram (EEG) | Graph Variational Auto Encoder (GVAE) | Graph deep learning
مقاله انگلیسی
6 Learning deep features for task-independent EEG-based biometric verification
یادگیری ویژگی های عمیق برای تأیید بیومتریک مبتنی بر EEG مستقل از وظیفه-2021
Face-based age estimation systems are commonly considered in biometric applications as well as in other fields such as forensics or healthcare. For security purposes, features extracted from the face can be used to verify or estimate the age of individuals in order to control their access to physical or logical resources. The main problem in using facial biometrics is its sensitivity, to acquisition (e.g. illumination, pose, occlusion, image quality, etc.), to face expression, and especially to potential attacks in unsupervised environments. In this work, we propose a robust modality using both random auditory stimulation and Deep-learning based age estimation, though individual perception (RaS-DeeP): (1) as a countermeasure to prevent attacks on face-based age estimation systems, but also (2) : as a complementary modality in a multimodal biometric system (i.e. face-sound perception) in order to improve the performances of face-based age estimation system. Used as countermeasure, we show that RaS-DeeP provides promising results with an EER value of 4.2%. On the other hand, when considering the multimodal system faceauditory perception, we show that, the performance of face age estimation system is enhanced with an EER of 3.3%. To evaluate the performance of multimodal system in real-time, 71 subjects from different age ranges achieving five repetitions, participated in our experiment.
keywords: Age estimation | Countermeasures | Forensics | Multimodal biometrics | Unsupervised biometrics | RaS-DeeP
مقاله انگلیسی
7 On the channel density of EEG signals for reliable biometric recognition
چگالی کانال سیگنال های EEG برای تشخیص بیومتریک قابل اعتماد-2021
Electroencephalography (EEG) provides appealing biometrics by encompassing unique attributes including robustness against forgery, privacy compliance, and aliveness detection. Among the main challenges in deploying EEG biometric systems in real-world applications, stability and usability are two important ones. They respectively reflect the capacity of the system to provide stable performance within and across different states, and the ease of use of the system. Previous studies indicate that the usability of an EEG biometric system is largely affected by the number of electrodes and reducing channel density is an effective way to enhance usability. However, it is still unclear what is the impact of channel density on recognition performance and stability. This study examines this issue for systems using different feature extraction and classification methods. Our results reveal a trade-off between channel density and stability. With low-density EEG, the recognition accuracy and stability are compromised to varying degrees. Based on the analysis, we propose a framework that integrates channel density augmentation, functional connectivity estimation and deep learning models for practical and stable EEG biometric systems. The framework helps to improve the stability of EEG biometric systems that use consumer-grade low channel density devices, while retaining the advantages of high usability.
Keywords: EEG biometrics | Data augmentation | Deep learning | Current source density
مقاله انگلیسی
8 Neural markers of suppression in impaired binocular vision
نشانگرهای عصبی سرکوب در اختلال بینایی دو چشمی-2021
Even after conventional patching treatment, individuals with a history of amblyopia typically lack good stereo vision. This is often attributed to atypical suppression between the eyes, yet the specific mechanism is still un- clear. Guided by computational models of binocular vision, we tested explicit predictions about how neural responses to contrast might differ in individuals with impaired binocular vision. Participants with a history of amblyopia (N = 25), and control participants with typical visual development (N = 19) took part in the study. Neural responses to different combinations of contrast in the left and right eyes, were measured using both electro encephalography (EEG) and functional magnetic resonance imaging (fMRI). Stimuli were sinusoidal gratings with a spatial frequency of 3c/deg, flickering at 4 Hz. In the fMRI experiment, we also ran population receptive field and retinotopic mapping sequences, and a phase-encoded localizer stimulus, to identify voxels in primary visual cortex (V1) sensitive to the main stimulus. Neural responses in both modalities increased monotonically with stimulus contrast. When measured with EEG, responses were attenuated in the weaker eye, consistent with a fixed tonic suppression of that eye. When measured with fMRI, a low contrast stimulus in the weaker eye substantially reduced the response to a high contrast stimulus in the stronger eye. This effect was stronger than when the stimulus-eye pairings were reversed, consistent with unbalanced dynamic suppression between the eyes. Measuring neural responses using different methods leads to different conclusions about visual differences in individuals with impaired binocular vision. Both of the atypical suppression effects may relate to binocular perceptual deficits, e.g. in stereopsis, and we anticipate that these measures could be informative for monitoring the progress of treatments aimed at recovering binocular vision.
Keywords: Dichoptic | fMRI | Interocular suppression | SSVEP | V1
مقاله انگلیسی
9 Analysis of factors that influence the performance of biometric systems based on EEG signals
تجزیه و تحلیل عوامل موثر بر عملکرد سیستم های بیومتریک بر اساس سیگنال های EEG-2021
Searching for new biometric traits is currently a necessity because traditional ones such as fingerprint, voice, or face are highly prone to forgery. For this reason, the study of bioelectric signals has the potential to develop new biometric systems. A motivation for using electroencephalogram signals is that they are unique to each person and are much more difficult to replicate than conventional biometrics. The objective of this study is to analyze the factors that influence the performance of a biometric system based on electroencephalogram signals. This work uses six different classifiers to compare several decomposition levels of the discrete wavelet transform as a preprocessing technique and also explores the importance of the recording time. These classifiers are Gaussian Naïve Bayes Classifier, K-Nearest Neighbors, Random Forest, AdaBoost, Support Vector Machine, and Multilayer Perceptron. This work proves that the decomposition level does not have a high impact on the overall result of the system. On the other hand, the recording time of electroencephalograms has a significant impact on the performance of the classifiers. It is worth mentioning that this study used two different datasets to validate the results. Finally, our experiments show that Support Vector Machine and AdaBoost are the bestclassifiers for this particular problem since they achieved a sensitivity, specificity, and accuracy of 85.94 ± 1.8, 99.55 ± 0.06, 99.12 ± 0.11 and 95.54 ± 0.53, 99.91 ± 0.01, and 99.83 ± 0.02 respectively.
Keywords: Biometrics | Electroencephalogram | Discrete Wavelet Transform | Performance factors
مقاله انگلیسی
10 Towards online applications of EEG biometrics using visual evoked potentials
در جهت کاربردهای آنلاین بیومتریک EEG با استفاده از پتانسیل های برانگیخته بصری-2021
Electroencephalogram (EEG)-based biometrics have attracted increasing attention in recent years. A few studies have used visual evoked potentials (VEPs) in EEG biometrics due to their high signal-to-noise ratio (SNR) and good stability. However, a systematic comparison of different types of VEPs is still lacking. Therefore, this study proposes a system framework for VEP-based biometrics. We quantitatively compared the performance of three types of VEP signals in person identification. Flash VEPs (f-VEPs), steady-state VEPs (ss-VEPs), and code- modulated VEPs (c-VEPs) measured from a group of 21 subjects on two different days were used to estimate the correct recognition rate (CRR). We adopted a template-matching-based identification algorithm that was developed for VEP detection in brain-computer interfaces (BCIs) for person identification. Furthermore, this study demonstrates an online person identification system using c-VEPs with a group of 15 subjects. Among the three methods, c-VEPs achieved the highest CRRs of 100% using 3.15-s VEP data (a 5.25-s duration including 2.1-s intervals) in the intra-session condition and 99.48% using 10.5-s VEP data (a 17.5-s duration including 7-s intervals) in the cross-session condition. The online system achieved a cross-session CRR of 98.93% using 10.5-s VEP data (a 14-s duration including 3.5-s intervals). A systematic comparison of the performance of the three types of VEP signals in EEG-based person identification revealed that the c-VEP paradigm achieved the highest CRRs. The online system further demonstrated high performance in practical applications. The proposed VEP- based biometric system obtained promising identification performance, showing great potential for online per- son identification applications in real life.
Keywords: Biometrics | Electroencephalography | Person identification | Visual evoked potentials | Pattern analysis
مقاله انگلیسی
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