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Wyniki wyszukiwania dla: intracranial EEG
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Independent dynamics of slow, intermediate, and fast intracranial EEG spectral activities during human memory formation
PublikacjaA wide spectrum of brain rhythms are engaged throughout the human cortex in cognitive functions. How the rhythms of various low and high frequencies are spatiotemporally coordinated across the human brain during memory processing is inconclusive. They can either be coordinated together across a wide range of the frequency spectrum or induced in specific bands. We used a large dataset of human intracranial electroencephalography...
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Independent dynamics of slow, intermediate, and fast intracranial EEG spectral activities during human memory formation
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Independent dynamics of low, intermediate, and high frequency spectral intracranial EEG activities during human memory formation
PublikacjaA wide spectrum of brain rhythms are engaged throughout the human cortex in cognitive functions. How the rhythms of various frequency ranges are coordinated across the space of the human cortex and time of memory processing is inconclusive. They can either be coordinated together across the frequency spectrum at the same cortical site and time or induced independently in particular bands. We used a large dataset of human intracranial...
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Noninvasive Measurement of Intracranial Pressure: Is It Possible?
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CLINICAL EEG AND NEUROSCIENCE
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Balance recognition on the basis of EEG measurement.
PublikacjaAlthough electroencephalography (EEG) is not typically used for verifying the sense of balance, it can be used for analysing cortical signals responsible for this phenomenon. Simple balance tasks can be proposed as a good indicator of whether the sense of balance is acting more or less actively. This article presents preliminary results for the potential of using EEG to balance sensing....
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Metody redukcji artefaktów w zapisie EEG.
PublikacjaPrzegląd i opis metod badania EEG jego uwarunkowań technicznych oraz problemy z tym związane. Dokonano przeglądu metod pozwalających na zredukowanie bądź eliminacje artefaktów w zapisie EEG.
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Decoding imagined speech for EEG-based BCI
PublikacjaBrain–computer interfaces (BCIs) are systems that transform the brain's electrical activity into commands to control a device. To create a BCI, it is necessary to establish the relationship between a certain stimulus, internal or external, and the brain activity it provokes. A common approach in BCIs is motor imagery, which involves imagining limb movement. Unfortunately, this approach allows few commands. As an alternative, this...
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Tensor Decomposition for Imagined Speech Discrimination in EEG
PublikacjaMost of the researches in Electroencephalogram(EEG)-based Brain-Computer Interfaces (BCI) are focused on the use of motor imagery. As an attempt to improve the control of these interfaces, the use of language instead of movement has been recently explored, in the form of imagined speech. This work aims for the discrimination of imagined words in electroencephalogram signals. For this purpose, the analysis of multiple variables...
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Labeler-hot Detection of EEG Epileptic Transients
PublikacjaPreventing early progression of epilepsy and sothe severity of seizures requires effective diagnosis. Epileptictransients indicate the ability to develop seizures but humansoverlook such brief events in an electroencephalogram (EEG)what compromises patient treatment. Traditionally, trainingof the EEG event detection algorithms has relied on groundtruth labels, obtained from the consensus...
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Can we avoid intracranial complications of chronic otitis media?
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Behavioral state classification in epileptic brain using intracranial electrophysiology
PublikacjaOBJECTIVE: Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. APPROACH: Data from seven patients (age [Formula: see text], 4 women) who underwent intracranial depth electrode...
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Transfer learning in imagined speech EEG-based BCIs
PublikacjaThe Brain–Computer Interfaces (BCI) based on electroencephalograms (EEG) are systems which aim is to provide a communication channel to any person with a computer, initially it was proposed to aid people with disabilities, but actually wider applications have been proposed. These devices allow to send messages or to control devices using the brain signals. There are different neuro-paradigms which evoke brain signals of interest...
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Intracranial electrophysiological recordings from the human brain during memory tasks with pupillometry
PublikacjaData comprise intracranial EEG (iEEG) brain activity represented by stereo EEG (sEEG) signals, recorded from over 100 electrode channels implanted in any one patient across various brain regions. The iEEG signals were recorded in epilepsy patients (N=10) undergoing invasive monitoring and localization of seizures when they were performing a battery of four memory tasks lasting approx. 1 hour in total. Gaze tracking on the task...
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Total DNA methylation as a biomarker of DNA damage and tumor malignancy in intracranial meningiomas
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MACHINE LEARNING APPLICATIONS IN RECOGNIZING HUMAN EMOTIONS BASED ON THE EEG
PublikacjaThis study examined the machine learning-based approach allowing the recognition of human emotional states with the use of EEG signals. After a short introduction to the fundamentals of electroencephalography and neural oscillations, the two-dimensional valence-arousal Russell’s model of emotion was described. Next, we present the assumptions of the performed EEG experiment. Detail aspects of the data sanitization including preprocessing,...
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Comparison of the effectiveness of automatic EEG signal class separation algorithms
PublikacjaIn this paper, an algorithm for automatic brain activity class identification of EEG (electroencephalographic) signals is presented. EEG signals are gathered from seventeen subjects performing one of the three tasks: resting, watching a music video and playing a simple logic game. The methodology applied consists of several steps, namely: signal acquisition, signal processing utilizing z-score normalization, parametrization and...
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Systematic Literature Review for Emotion Recognition from EEG Signals
PublikacjaResearchers have recently become increasingly interested in recognizing emotions from electroencephalogram (EEG) signals and many studies utilizing different approaches have been conducted in this field. For the purposes of this work, we performed a systematic literature review including over 40 articles in order to identify the best set of methods for the emotion recognition problem. Our work collects information about the most...
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Systematic Literature Review for Emotion Recognition from EEG Signals
PublikacjaResearchers have recently become increasingly interested in recognizing emotions from electroencephalogram (EEG) signals and many studies utilizing different approaches have been conducted in this field. For the purposes of this work, we performed a systematic literature review including over 40 articles in order to identify the best set of methods for the emotion recognition problem. Our work collects information about the most...
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Automatic Clustering of EEG-Based Data Associated with Brain Activity
PublikacjaThe aim of this paper is to present a system for automatic assigning electroencephalographic (EEG) signals to appropriate classes associated with brain activity. The EEG signals are acquired from a headset consisting of 14 electrodes placed on skull. Data gathered are first processed by the Independent Component Analysis algorithm to obtain estimates of signals generated by primary sources reflecting the activity of the brain....
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Comparison of Classification Methods for EEG Signals of Real and Imaginary Motion
PublikacjaThe classification of EEG signals provides an important element of brain-computer interface (BCI) applications, underlying an efficient interaction between a human and a computer application. The BCI applications can be especially useful for people with disabilities. Numerous experiments aim at recognition of motion intent of left or right hand being useful for locked-in-state or paralyzed subjects in controlling computer applications....
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Deep learning approach on surface EEG based Brain Computer Interface
PublikacjaIn this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this...
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Method for Clustering of Brain Activity Data Derived from EEG Signals
PublikacjaA method for assessing separability of EEG signals associated with three classes of brain activity is proposed. The EEG signals are acquired from 23 subjects, gathered from a headset consisting of 14 electrodes. Data are processed by applying Discrete Wavelet Transform (DWT) for the signal analysis and an autoencoder neural network for the brain activity separation. Processing involves 74 wavelets from 3 DWT families: Coiflets,...
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Comparison of Methods for Real and Imaginary Motion Classification from EEG Signals
PublikacjaA method for feature extraction and results of classification of EEG signals obtained from performed and imagined motion are presented. A set of 615 features was obtained to serve for the recognition of type and laterality of motion using 8 different classifications approaches. A comparison of achieved classifiers accuracy is presented in the paper, and then conclusions and discussion are provided. Among applied algorithms the...
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Rough Set-Based Classification of EEG Signals Related to Real and Imagery Motion
PublikacjaA rough set-based approach to classification of EEG signals registered while subjects were performing real and imagery motions is presented in the paper. The appropriate subset of EEG channels is selected, the recordings are segmented, and features are extracted, based on time-frequency decomposition of the signal. Rough set classifier is trained in several scenarios, comparing accuracy of classification for real and imagery motion....
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Pursuing the Deep-Learning-Based Classification of Exposed and Imagined Colors from EEG
PublikacjaEEG-based brain-computer interfaces are systems aiming to integrate disabled people into their environments. Nevertheless, their control could not be intuitive or depend on an active external stimulator to generate the responses for interacting with it. Targeting the second issue, a novel paradigm is explored in this paper, which depends on a passive stimulus by measuring the EEG responses of a subject to the primary colors (red,...
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Brain-computer interaction based on EEG signal and gaze-tracking information = Analiza interackji mózg-komputer wykorzystująca sygnał EEg i informacje z systemu śledzenia punktu fiksacji wzroku
PublikacjaThe article presents an attempt to integrate EEG signal analysis with information about human visual activities, i.e. gaze fixation point. The results from gaze-tracking-based measurement were combined with the standard EEG analysis. A search for correlation between the brain activity and the region of the screen observed by the user was performed. The preliminary stage of the study consists in electrooculography (EOG) signal processing....
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Near Complete Giant Intracranial Aneurysm Mimicking Anaplastic Glioma: A Rare Case Report And Surgical Management
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Rating by detection: an artifact detection protocol for rating EEG quality with average event duration
PublikacjaQuantitative evaluation protocols are critical for the development of algorithms that remove artifacts from real EEG optimally. However, visually inspecting the real EEG to select the top-performing artifact removal pipeline is infeasible while hand-crafted EEG data allow assessing artifact removal configurations only in a simulated environment. This study proposes a novel, principled approach for quantitatively evaluating algorithmically...
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Analysis of the Capability of Deep Learning Algorithms for EEG-based Brain-Computer Interface Implementation
PublikacjaMachine learning models have received significant attention for their exceptional performance in classifying electroencephalography (EEG) data. They have proven to be highly effective in extracting intricate patterns and features from the raw signal data, thereby contributing to their success in EEG classification tasks. In this study, we explore the possibilities of utilizing contemporary machine learning algorithms in decoding...
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Intra-subject class-incremental deep learning approach for EEG-based imagined speech recognition
PublikacjaBrain–computer interfaces (BCIs) aim to decode brain signals and transform them into commands for device operation. The present study aimed to decode the brain activity during imagined speech. The BCI must identify imagined words within a given vocabulary and thus perform the requested action. A possible scenario when using this approach is the gradual addition of new words to the vocabulary using incremental learning methods....
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Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications
PublikacjaRough set-based approach to the classification of EEG signals of real and imaginary motion is presented. The pre-processing and signal parametrization procedures are described, the rough set theory is briefly introduced, and several classification scenarios and parameters selection methods are proposed. Classification results are provided and discussed with their potential utilization for multimedia applications controlled by the...
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Associations between intracranial pressure, intraocular pressure and mean arterial pressure in patients with traumatic and non-traumatic brain injuries
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Comparison of two cervical collars on the intracranial pressure measured indirectly based on the thickness of the optic nerve sheath. Preliminary data
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The Effect of PEG Concentration on the Release Rate of Cisplatin from PEG-Modified Silica Xerogels
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Wspomaganie komunikacji w procesie neurorehabilitacji z wykorzystaniem śledzenia wzroku i analizy sygnałów EEG
PublikacjaW pracy przedstawiono charakterystykę systemu do wspomagania komunikacji w procesie neurorehabilitacji osób w stanie ograniczonej świadomości. Przygotowana aplikacja komputerowa wykorzystuje metodę śledzenia wzroku wspomaganą analizą sygnału EEG. W pracy podano genezę powstania systemu, scharakteryzowano zaimplementowane ćwiczenia oraz pozostałe funkcjonalności, a także zamieszczono wyniki wstępnych badań dokonanych w kilku polskich...
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Techniki śródoperacyjnego monitorowania EKG
PublikacjaZ badania EKG lekarze uzyskują szereg cennych informacji niezbędnych do prawidłowego podejmowanie ważnych decyzji terapeutycznych. EKG stało się pierwszym parametrem monitorowanym standardowo i w sposób ciągły podczas znieczulenia. Współczesna technika umożliwia nie tylko ciągłe monitorowanie zapisu EKG, a także transmisję danych on-line do dowolnego miejsca w szpitalu, i ich cyfrową rejestrację. Najistotniejszym kryterium przydatności...
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Wykorzystanie rzeczywistego zapisu EEG w modelu potencjałów wywołanych do testowania algorytmów detekcji potencjałów wzrokowych.
PublikacjaPrzedstawiona praca dotyczy kontynuacji badań modelowych, gdzie syntetyczna aktywność spontaniczna została zastąpiona sygnałem rzeczywistym. Ocenie podlega uprzednio stworzony model aktywności spontanicznej. Weryfikacji ulega przydatność algorytmu detekcji.
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A Computationally Efficient Model for Predicting Successful Memory Encoding Using Machine-Learning-based EEG Channel Selection
PublikacjaComputational cost is an important consideration for memory encoding prediction models that use data from dozens of implanted electrodes. We propose a method to reduce computational expense by selecting a subset of all the electrodes to build the prediction model. The electrodes were selected based on their likelihood of measuring brain activity useful for predicting memory encoding better than chance (in terms of AUC). A logistic...
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Current understanding of the effects of inspiratory resistance on the interactions between systemic blood pressure, cerebral perfusion, intracranial pressure, and cerebrospinal fluid dynamics
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ENG AGR-JABOTICABAL
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EGE ACADEMIC REVIEW
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TCE-THE CHEM ENG
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Intracranial hemorrhage detection in 3D computed tomography images using a bi-directional long short-term memory network-based modified genetic algorithm
PublikacjaIntroduction: Intracranial hemorrhage detection in 3D Computed Tomography (CT) brain images has gained more attention in the research community. The major issue to deal with the 3D CT brain images is scarce and hard to obtain the labelled data with better recognition results. Methods: To overcome the aforementioned problem, a new model has been implemented in this research manuscript. After acquiring the images from the Radiological...
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The Influence of PEG on Morphology of Polyurethane Tissue Scaffold
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Jednokanałowy rejstrator EKG z transmisją radiową.
PublikacjaW referacie zaprezentowano jednokanałowy rejestrator EKG z możliwością przesyłania pomiarów do komputera PC za pomocą transmisji radiowej. Dzięki zastosowaniu układów o niskim poborze mocy, oraz mikrokonwertera ADuC812 otrzymano urządzenie charakteryzujące się dobrymi parametrami pomiarowymi, przy niewielkiej komplikacji układowej i małym poborze prądu. Zastosowanie gotowych modułów teletransmisyjnych uprościło konstrukcję...
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Arm EMG Wavelet-Based Denoising System
PublikacjaThese paper presents research results of muscle EMG signal denoising. In the same time two muscles were examined - an adductor muscle (biceps brachii) and an abductor muscle (tricpeps brachii). The EMG signal was filtered using the wavelet transform technique, having selected the crucial parameters as: wavelet basis function (Daubechies 4), 10 th decomposition level, threshold selection algorithm (Heurestic) and a sln rescaling...
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Optimal ECG lead for deriving respiratory signal
PublikacjaEDR is an interesting measuring technique that allows an indirect assessment of respiratory activity. This is an alternative solution to direct methods that are based on the measurement of air flow, which require a specialized sensor or even a system. However, due to inter-personal anatomical differences, the optimal ECG lead (placement of the electrodes) ensuring the best EDR signal quality is not fixed. An influence of ECG lead...
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A body position influence on ECG derived respiration
PublikacjaAn influence of a human body position on ECG derived respiration (EDR) signal is presented in the paper. Examinations were performed during deep, suspended and normal breathing for eight people in four different body positions. EDR and thoracic impedance signals were compared using correlation and standard deviation coefficients. Obtained results have shown that it is possible to monitor breath activity of people being in different...
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Smart Weighing Scale with Feet-Sampled ECG
PublikacjaIn a smart home, health and well-being monitoring systems could be embedded in everyday devices providing a pervasive care. A home bathroom scale is an example of such a device, typically used to measure body weight and very often its composition (e.g. body water/fat percentage). In this paper, we analyzed a potential use of the bathroom scale to measure electrocardiogram (ECG) from electrodes located on the scale's tile. In particular...