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نتیجه جستجو - EEG

تعداد مقالات یافته شده: 60
ردیف عنوان نوع
11 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
12 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
13 Evaluating the effectiveness of biometric sensors and their signal features for classifying human experience in virtual environments
ارزیابی اثربخشی سنسورهای بیومتریک و ویژگی های سیگنال آنها برای طبقه بندی تجربه انسان در محیط های مجازی-2021
Built environments play an essential role in our day-to-day lives since people spend more than 85% of their times indoors. Previous studies at the conjunction of neuroscience and architecture confirmed the impact of architectural design features on varying human experience, which propelled researchers to study the improvement of human experience in built environments using quantitative methods such as biometric sensing. However, a notable gap in the knowledge persists as researchers are faced with sensors that are commonly used in the neuroscience domain, resulting in a disconnect regarding the selection of effective sensors that can be used to measure human experience in designed spaces. This issue is magnified when considering the variety of sensor signal features that have been proposed and used in previous studies. This study builds on data captured during a series of user studies conducted to measure subjects’ physiological responses in designed spaces using the combination of virtual environments and biometric sensing. This study focuses on the data analysis of the collected sensor data to identify effective sensors and their signal features in classifying human experience. To that end, we used a feature attribution model (i.e., SHAP), which calculates the importance of each signal feature in terms of Shapley values. Results show that electroencephalography (EEG) sensors are more effective as compared to galvanic skin response (GSR) and photoplethysmogram (PPG) (i.e., achieving the highest SHAP values among the three at 3.55 as compared to 0.34 for GSR and 0.21 for PPG) when capturing human experience in alternate designed spaces. For EEG, signal features calculated from the back channels (occipital and parietal areas) were found to possess comparable effectiveness as the frontal channel (i.e., have similar mean SHAP values per channel). In addition, frontal and occipital asymmetry were found to be effective in identifying human experience in designed spaces.
Keywords: Architectural design | Feature attribution | Data-driven methods | Human experience | Virtual environments
مقاله انگلیسی
14 A multimodal-Siamese Neural Network (mSNN) for person verification using signatures and EEG
شبکه عصبی چند حالته سیامی (mSNN) برای تأیید شخص با استفاده از امضا و EEG-2021
Signatures have long been considered to be one of the most accepted and practical means of user verification, despite being vulnerable to skilled forgers. In contrast, EEG signals have more recently been shown to be more difficult to replicate, and to provide better biometric information in response to known a stimulus. In this paper, we propose combining these two biometric traits using a multimodal Siamese Neural Network (mSNN) for improved user verification. The proposed mSNN network learns discriminative temporal and spatial features from the EEG signals using an EEG encoder and from the offline signatures using an image encoder. Features of the two encoders are fused into a common feature space for further processing. A Siamese network then employs a distance metric based on the similarity and dissimilarity of the input features to produce the verification results. The proposed model is evaluated on a dataset of 70 users, comprised of 1400 unique samples. The novel mSNN model achieves a 98.57% classification accuracy with a 99.29% True Positive Rate (TPR) and False Acceptance Rate (FAR) of 2.14%, outperforming the current state-of-the-art by 12.86% (in absolute terms). This proposed network architecture may also be applicable to the fusion of other neurological data sources to build robust biometric verification or diagnostic systems with limited data size.
Keywords: User verification | Multimodal | EEG | Siamese Neural Network | LSTM | CNN
مقاله انگلیسی
15 What electrophysiology tells us about Alzheimer’s disease: a window into the synchronization and connectivity of brain neurons
آنچه الکتروفیزیولوژی در مورد بیماری آلزایمر به ما می گوید: پنجره ای برای هماهنگ سازی و اتصال نورون های مغز-2020
Electrophysiology provides a real-time readout of neural functions and network capability in different brain states, on temporal (fractions of milliseconds) and spatial (micro, meso, and macro) scales unmet by other methodologies. However, current international guidelines do not endorse the use of electroencephalographic (EEG)/magnetoencephalographic (MEG) biomarkers in clinical trials performed in patients with Alzheimer’s disease (AD), despite a surge in recent validated evidence. This position paper of the ISTAART Electrophysiology Professional Interest Area endorses consolidated and translational electrophysiological techniques applied to both experimental animal models of AD and patients, to probe the effects of AD neuropathology (i.e., brain amyloidosis, tauopathy, and neurodegeneration) on neurophysiological mechanisms underpinning neural excitation/inhibition and neurotransmission as well as brain network dynamics, synchronization, and functional connectivity, reflecting thalamocortical and corticocortical residual capacity. Converging evidence shows relationships between abnormalities in EEG/MEG markers and cognitive deficits in groups of AD patients at different disease stages. The supporting evidence for the application of electrophysiology in AD clinical research as well as drug discovery pathways warrants an international initiative to include the use of EEG/MEG biomarkers in the main multicentric projects planned in AD patients, to produce conclusive findings challenging the present regulatory requirements and guidelines for AD studies.
Keywords: The Alzheimer’s Association International | Society to Advance Alzheimer’s Research | and Treatment (ISTAART) | Alzheimer’s disease (AD) | Electroencephalography and | magnetoencephalography (EEG and MEG) | Resting-state condition | Event-related potentials and magnetic fields | Preclinical and clinical research
مقاله انگلیسی
16 Intranasal oxytocin enhances EEG mu rhythm desynchronization during execution and observation of social action: An exploratory study
اکسی توسین داخل رحمی باعث می شود که EEG mu رطوبت زدایی در حین اجرا و مشاهده اقدامات اجتماعی تقویت شود: یک مطالعه اکتشافی-2020
Intranasal administration of oxytocin (OT) has been found to facilitate prosocial behaviors, emotion recognition and cooperation between individuals. Recent electroencephalography (EEG) investigations have reported enhanced mu rhythm (alpha: 8–13 Hz; beta: 15–25 Hz) desynchronization during the observation of biological motion and stimuli probing social synchrony after the administration of intranasal OT. This hormone may therefore target a network of cortical circuits involved in higher cognitive functions, including the mirror neuron system (MNS). Here, in a double-blind, placebo-controlled, between-subjects exploratory study, we investigated whether intranasal OT modulates the cortical activity from sensorimotor areas during the observation and the execution of social and non-social grasping actions. Participants underwent EEG testing after receiving a single dose (24 IU) of either intranasal OT or placebo. Results revealed an enhancement of alpha - but not beta - desynchronization during observation and execution of social grasps, especially over central and parietal electrodes, in participants who received OT (OT group). No differences between the social and non-social condition were found in the control group (CTRL group). Moreover, we found a significant difference over the cortical central-parietal region between the OT and CTRL group only within the social condition. These results suggest a possible action of intranasal OT on sensorimotor circuits involved in social perception and action understanding, which might contribute to facilitate the prosocial effects typically reported by behavioral studies.
Keywords: Oxytocin | ERD | Mirror neuron system | Grasping actions | Electroencephalogram
مقاله انگلیسی
17 An analysis of cybersecurity in Dutch annual reports of listed companies
تجزیه و تحلیل امنیت سایبری در گزارش های سالانه هلندی شرکت های فهرست شده-2020
Keywords: Cybersecurity Financial law Annual report Information sharing Security regulationIn this paper we study the disclosure of cybersecurity information in Dutch annual reports, such as cybersecurity measures and cyber incidents, from a financial law and economics perspective. We start our discussion with an analysis of the requirements in financial law to disclose cybersecurity information in annual reports. Hereafter, we discuss the incentives for the board regarding disclosing cybersecurity related information and its effect on stakeholders and shareholders. We draft hypotheses regarding the actual disclosure of cy- bersecurity information and propose a research design of an exploring empirical study. The results of our study show that although there is no strict legal obligation to do so, 87% of the companies mention cybersecurity or similar words in their annual report in 2018. However, only 4 out of 75 companies disclosed more than six specific cybersecurity measures, while openness would generate the highest surplus for society from a social welfare perspective. Some major Dutch banks and employment agencies did not disclose any specific information with regard to their cybersecurity strategy, while those companies are highly vulnerable for cybersecurity incidents. This hampers the protection of creditors, investors and other stakeholders. Our analysis aims to propel the debate on stimulation of self-regulation or possible obligations in financial law concerning cybersecurity in annual reports.© 2020 E.V.A. Eijkelenboom and B.F.H. Nieuwesteeg. Published by Elsevier Ltd. All rightsreserved.
Keywords: Cybersecurity | Financial law | Annual report | Information sharing | Security regulation
مقاله انگلیسی
18 A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome
یک سیستم هوش مصنوعی پیشنهادی برای حمایت از تشخیص صرع بر اساس طبقه بندی صرع 2017: نشان داده شده توسط سندرم دراوت-2020
Purpose: The 2017 epilepsy and seizure diagnosis framework emphasizes epilepsy syndromes and the etiologybased approach.We developed a propositional artificial intelligence (AI) system based on the above concepts to support physicians in the diagnosis of epilepsy. Methods:We analyzed and built ontology knowledge for the classification of seizure patterns, epilepsy, epilepsy syndrome, and etiologies. Protégé ontology tool was applied in this study. In order to enable the system to be close to the inferential thinking of clinical experts, we classified and constructed knowledge of other epilepsyrelated knowledge, including comorbidities, epilepsy imitators, epilepsy descriptors, characteristic electroencephalography (EEG) findings, treatments, etc. We used the OntologyWeb Language with Description Logic (OWL-DL) and Semantic Web Rule Language (SWRL) to design rules for expressing the relationship between these ontologies. Results: Dravet syndrome was taken as an illustration for epilepsy syndromes implementation.We designed an interface for the physician to enter the various characteristics of the patients. Clinical data of an 18-year-old boy with epilepsy was applied to the AI system. Through SWRL and reasoning engine Drools execution, we successfully demonstrate the process of differential diagnosis. Conclusion: We developed a propositional AI system by using the OWL-DL/SWRL approach to deal with the complexity of current epilepsy diagnosis. The experience of this system, centered on the clinical epilepsy syndromes, paves a path to construct an AI system for further complicated epilepsy diagnosis.
Keywords: Epilepsy syndrome | Etiology | OWL-DL | Protégé | Seizure classification | SemanticWeb Rule Language
مقاله انگلیسی
19 Behavior of crossover operators in NSGA-III for large-scale optimization problems
رفتار اپراتورهای متقاطع در NSGA-III برای مسائل بهینه سازی در مقیاس بزرگ-2020
Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usu- ally meet the requirements for online data processing because of their high compu- tational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algo- rithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable com- putational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simu- lated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the con- cept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.
Keywords: Electroencephalography | Large-scale optimization | Big data optimization | Evolutionary multi-objective optimization | NSGA-III | Crossover operator | Performance analysis
مقاله انگلیسی
20 Revisiting the value of polysomnographic data in insomnia: more than meets the eye
بازنگری ارزش داده سندرم آپنه در بی خوابی: بیش از ملاقات چشم-2020
Background: Polysomnography (PSG) is not recommended as a diagnostic tool in insomnia. However, this consensual approach might be tempered in the light of two ongoing transformations in sleep research: big data and artificial intelligence (AI). Method: We analyzed the PSG of 347 patients with chronic insomnia, including 59 with Sleep State Misperception (SSM) and 288 without (INS). 89 good sleepers (GS) were used as controls. PSGs were compared regarding: (1) macroscopic indexes derived from the hypnogram, (2) mesoscopic indexes extracted from the electroencephalographic (EEG) spectrum, (3) sleep microstructure (slow waves, spindles). We used supervised algorithms to differentiate patients from GS. Results: Macroscopic features illustrate the insomnia conundrum, with SSM patients displaying similar sleep metrics as GS, whereas INS patients show a deteriorated sleep. However, both SSM and INS patients showed marked differences in EEG spectral components (meso) compared to GS, with reduced power in the delta band and increased power in the theta/alpha, sigma and beta bands. INS and SSM patients showed decreased spectral slope in NREM. INS and SSM patients also differed from GS in sleep microstructure with fewer and slower slow waves and more and faster sleep spindles. Importantly, SSM and INS patients were almost indistinguishable at the meso and micro levels. Accordingly, unsupervised classifiers can reliably categorize insomnia patients and GS (Cohens k ¼ 0.87) but fail to tease apart SSM and INS patients when restricting classifiers to micro and meso features (k¼0.004). Conclusion: AI analyses of PSG recordings can help moving insomnia diagnosis beyond subjective complaints and shed light on the physiological substrate of insomnia.
Keywords: Artificial intelligence | Machine learning | Insomnia | Polysomnography | REM | NREM sleep
مقاله انگلیسی
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