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ردیف | عنوان | نوع |
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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 |
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 |
3 |
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 |
4 |
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 |
5 |
سریعترین واسط مغز و کامپیوتر جهان: ترکیب کد EEG2 با یادگیری عمیق
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 15 - تعداد صفحات فایل doc فارسی: 29 در این مقاله روش جدیدی بر اساس یادگیری عمیق برای کدگشایی اطلاعات حسیِ حاصل از الکتروانسفالوگرامهایی (EEG) که به صورت غیرتهاجمی ثبت شدهاند، ارائه میدهیم. این روش را میتوان در رابطهای مغز و کامپیوتر (BCI) غیرفعال برای پیشبینی ویژگیهای یک محرک بصری که فرد مشاهده میکند، به کار برد و یا میتوان برای کنترل فعالانهی کاربردهای BCI از آن استفاده کرد. هر دو سناریو مورد آزمایش قرار گرفتند، بدین ترتیب که متوسط نرخ انتقال اطلاعات (ITR) برابر با 701 بیت بر دقیقه برای روش BCI غیرفعال به دست آمد و بهترین سوژه به ITR آنلاین برابر با 1237 بیت بر دقیقه دست یافت. علاوه بر این، امکان تشخیص 500000 محرک بصری مختلف بر اساس تنها 2 ثانیه از اطلاعات EEG با دقت تا 100% را میسر ساخت. هنگامی که این روش در یک BCI خودگام آسنکرون برای هجی کردن به کار برده شد، متوسط نرخ سودمندی برابر با 175 بیت بر دقیقه به دست آمد که متناظر با به طور متوسط 35 حرف بدون خطا در هر دقیقه است. از آنجایی که اطلاعاتی که این روش استخراج میکند، بیش از سه برابرِ سریعترین روش قبلی است، نشان میدهیم که سیگنالهای EEG اطلاعات بیشتری نسبت به مقداری که معمولا فرض میشود، انتقال میدهند. در نهایت یک اثر حداکثر مشاهده کردیم به طوری که محتوای اطلاعات در EEG از آن چیزی که برای کنترل BCI لازم است، فراتر میرود و بنابراین در این مورد بحث میکنیم که آیا تحقیقات BCI به نقطهای رسیدهاند که دیگر نمیتوان عملکرد کنترل BCI بصری غیرتهاجمی را به طور قابل توجهی بهبود بخشید یا خیر. |
مقاله ترجمه شده |
6 |
Percolation theory for the recognition of patterns in topographic images of the cortical activity
نظریه انباشت برای تشخیص الگوهای موجود در تصاویر توپوگرافی از فعالیت قشر مغز-2019 Electroencephalogram (EEG) is one of the mechanisms used to collect complex data. Its use includes evaluating
neurological disorders, investigating brain function and correlations between EEG signals and real or imagined
movements. The Topographic Image of Cortical Activity (TICA) records obtained by the EEG make it possible to
observe, through color discrimination, the cortical areas that represent greater or lesser activity. Percolation
Theory (PT) reveals properties on the aspects of fluid spreading from a central point, these properties being
related to the aspects of the medium, topological characteristics and ease of penetration of a fluid in materials.
The hypothesis presented so far considers that synaptic activities originate in points and spread from them,
causing different areas of the brain to interact in a diffusive associative behavior, generating electric and
magnetic fields by the currents that spread through the brain tissue and have an effect on the scalp sensors. Brain
areas spatially separated create large-scale dynamic networks that are described by functional and effective
connectivity. The proposition is that this phenomenon behaves like a fluidic spreading, so we can use the PT,
through the topological analysis we detect specific signatures related to neural phenomena that manifest changes
in the behavior of synaptic diffusion. This signature must be characterized by the Fractal Dimension (FD) values
of the scattering clusters, these values will be used as properties in the k-Nearest Neighbors (kNN) method, an
TICA will be categorized according to the degree of similarity to the preexisting patterns. In this context, our
hypothesis will consolidate as a more computational resource in the service of medicine and another way that
opens with the possibility of analysis and detailed inferences of the brain through TICA that go beyond a simply
visual observation, as it happens in the present day. Keywords: Electroencephalogram | Cortical topography | Percolation theory |
مقاله انگلیسی |
7 |
Neural-signal electroencephalogram (EEG) methods to improve human-building interaction under different indoor air quality
روش های الکتروانسفالوگرام سیگنال عصبی (EEG) برای بهبود تعامل انسان سازی با کیفیت هوای متفاوت داخلی-2019 In this study, neural-signal electroencephalogram (EEG) methods to improve human-building interaction under different indoor air quality conditions were investigated. Experiment was conducted to study cor- relations between EEG frequency bands and subjective perception as well as task performance. Machine learning-based EEG pattern recognition methods as feedback mechanisms were also investigated. Results showed that EEG theta band (4–8 Hz) correlated with subjective perceptions, and EEG alpha band (8–13 Hz) correlated with task performance. These EEG indices could be utilized as more objective metrics in addition to questionnaire and task-based metrics. For the machine learning-based EEG pattern recog- nition methods, the linear discriminant analysis (LDA) and support vector machine (SVM) classifiers can classify mental states under different indoor air quality conditions with high accuracy. In general, the EEG theta and alpha bands as more objective indices and the machine learning-based EEG pattern recog- nition methods as real-time feedback mechanisms have good potential to improve the human-building interaction. Keywords: Electroencephalogram (EEG) | Machine learning | Human-building interaction | Indoor air quality | Short-term performance |
مقاله انگلیسی |
8 |
Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders
بررسی حجم کار ذهنی شناختی از طریق سیگنال های EEG و یک طبقه بندی یادگیری عمیق گروه بر اساس خودرمزگذار حذف نویز-2019 To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment,
we propose a novel human mental workload (MW) recognizer based on deep learning principles and
utilizing the features of the electroencephalogram (EEG). To determine personalized properties in high dimensional
EEG indicators, we introduce a feature mapping layer in stacked denoising autoencoder (SDAE) that is
capable of preserving the local information in EEG dynamics. The ensemble classifier is then built via the subjectspecific
integrated deep learning committee, and adapts to the cognitive properties of a specific human operator
and alleviates inter-subject feature variations. We validate our algorithms and the ensemble SDAE classifier with
local information preservation (denoted by EL-SDAE) on an EEG database collected during the execution of
complex human-machine tasks. The classification performance indicates that the EL-SDAE outperforms several
classical MW estimators when its optimal network architecture has been identified. Keywords: Mental workload | Human-machine system | Electroencephalogram | Stacked denoising autoencoder | Deep learning |
مقاله انگلیسی |
9 |
EEG signal analysis for epileptic seizures detection by applying Data Mining techniques
تجزیه و تحلیل سیگنال EEG برای تشخیص صرع با استفاده از تکنیک داده کاوی-2019 Epilepsy is a chronic neurological disorder characterized by frequent seizures, which severely impact the quality of life of epilepsy patients and sometimes are accompanied by loss of consciousness. The most widely accepted and used tool by epileptologists to identify seizures and diagnose epilepsy is the ElectroEncephaloGram (EEG). Seizure detec- tion on EEG signals is a long process, which is done manually by epileptologists. This paper describes how to analyze EEG signal using Data Mining methods and techniques with the main objective of automatically detect a seizure within EEG signals. We have designed and developed a multipurpose and extendable tool for feature extraction from time series data, named Training Builder. Our trained classifier, based on signal processing, sliding window paradigm, features extraction and selection, and Support Vector Machines, showed excel- lent results, reaching an accuracy of over 99% during the test made on publicly available EEG datasets. Keywords: Data Mining | Epilepsy | Electroencephalogram | Support Vector Machine | Signal processing | Sliding window |
مقاله انگلیسی |
10 |
BIARAM: A process for analyzing correlated brain regions using association rule mining
BIARAM: یک فرایند برای تحلیل مناطق مرتبط مغز با استفاده از کاوش قانون انجمنی-2018 Background and objective: Because examining correlated (vs. individual) brain activity is a superior
method for locating neural correlates of a stimulus, using a network approach for analyzing brain activity
is gaining interest. In this study, we propose and illustrate the use of association rule mining (ARM) to
analyze brain regions that are activated simultaneously. ARM is commonly used in marketing and other
disciplines to help determine items that might be purchased together. We apply this technique toward
identifying correlated brain regions that may respond simultaneously to specific stimuli. Our objective is
to introduce ARM, describe a process for converting neural images into viable datasets (for analyses), and
suggest how to apply this process for generating insights about the brain’s responses to specific stimuli
(e.g. technology-associated interruptions).
Methods: We analyze electroencephalogram (EEG) data collected from 46 participants; convert brain
waves into images via a source localization algorithm known as sLORETA (i.e., standardized low-resolution
brain electromagnetic tomography); reorganize these into a “transactional” dataset; and generate associ
ation rules through ARM.
Results: We compare the results with more conventional methods for analyzing neuroimaging data. We
show that there is a stronger correlation between frontal lobe and sublobar/insula regions after interrup
tions. This result would not be obvious from independent analysis of each region.
Conclusions: The main contribution of this paper is introducing ARM as a method for analyzing multi
ple images. We suggest that the biomedical community may apply this commonly available data mining
technique to develop further insights about correlated regions affected by specific stimuli.
Keywords: Association rule mining ، Interruptions ، Electroencephalogram (EEG) ، Neuroimaging |
مقاله انگلیسی |