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    Wyświetlanie 1-5 z 5
    Tytuł:
    Personal identification based on brain networks of EEG signals
    Autorzy:
    Kong, W.
    Jiang, B.
    Fan, Q.
    Zhu, L.
    Wei, X.
    Powiązania:
    https://bibliotekanauki.pl/articles/329856.pdf
    Data publikacji:
    2018
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    electroencephalogram signal
    personal identification
    brain network
    phase synchronization
    elektroencefalogram
    identyfikacja osobowa
    sieć mózgowa
    synchronizacja fazy
    Opis:
    Personal identification is particularly important in information security. There are numerous advantages of using electroencephalogram (EEG) signals for personal identification, such as uniqueness and anti-deceptiveness. Currently, many researchers focus on single-dataset personal identification, instead of the cross-dataset. In this paper, we propose a method for cross-dataset personal identification based on a brain network of EEG signals. First, brain functional networks are constructed from the phase synchronization values between EEG channels. Then, some attributes of the brain networks including the degree of a node, the clustering coefficient and global efficiency are computed to form a new feature vector. Lastly, we utilize linear discriminant analysis (LDA) to classify the extracted features for personal identification. The performance of the method is quantitatively evaluated on four datasets involving different cognitive tasks: (i) a four-class motor imagery task dataset in BCI Competition IV (2008), (ii) a two-class motor imagery dataset in the BNCI Horizon 2020 project, (iii) a neuromarketing dataset recorded by our laboratory, (iv) a fatigue driving dataset recorded by our laboratory. Empirical results of this paper show that the average identification accuracy of each data set was higher than 0.95 and the best one achieved was 0.99, indicating a promising application in personal identification.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2018, 28, 4; 745-757
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Brain, mind and modern human identity – dilemmas and solutions
    Autorzy:
    Błaszak, Maciej
    Powiązania:
    https://bibliotekanauki.pl/articles/703008.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    predictive mind
    bayesian brain
    salience network
    central executive network
    default mode network
    human cognitive evolution
    Opis:
    Human brain is “the perfect guessing machine” (James V. Stone (2012) Vision and Brain, Cambridge, Mass: The MIT Press, p. 155), trying to interpret sensory data in the light of previous biases or beliefs. Bayesian inference is carried out by three complex networks of the human brain: salience network, central executive network, and default mode network. Their function is analysed both in neurotypical person and Attention Deficit Disorder. Modern human being having predictive brain and overloaded mind must develop social identity, whose evolution went probably through three stages: social selection based on punishment, sexual selection based on reputation, and group selection based on identity.
    Źródło:
    Nauka; 2017, 1
    1231-8515
    Pojawia się w:
    Nauka
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Neuroplasticity and Microglia Functions Applied in Dense Wireless Networks
    Autorzy:
    Kułacz, Łukasz
    Kliks, Adrian
    Powiązania:
    https://bibliotekanauki.pl/articles/308840.pdf
    Data publikacji:
    2019
    Wydawca:
    Instytut Łączności - Państwowy Instytut Badawczy
    Tematy:
    ad-hoc network
    brain inspired communication
    glial cells
    neurons
    Opis:
    This paper presents developments in the area of brain-inspired wireless communications relied upon in dense wireless networks. Classic approaches to network design are complemented, firstly, by the neuroplasticity feature enabling to add the learning ability to the network. Secondly, the microglia ability enabling to repair a network with damaged neurons is considered. When combined, these two functionalities guarantee a certain level of fault-tolerance and self-repair of the network. This work is inspired primarily by observations of extremely energy efficient functions of the brain, and of the role that microglia cells play in the active immune defense system. The concept is verified by computer simulations, where messages are transferred through a dense wireless network based on the assumption of minimized energy consumption. Simulation encompasses three different network topologies which show the impact that the location of microglia nodes and their quantity exerts on network performance. Based on the results achieved, some algorithm improvements and potential future work directions have been identified.
    Źródło:
    Journal of Telecommunications and Information Technology; 2019, 1; 39-46
    1509-4553
    1899-8852
    Pojawia się w:
    Journal of Telecommunications and Information Technology
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Convolutional neural networks for P300 signal detection applied to brain computer interface
    Autorzy:
    Riyad, Mouad
    Khalil, Mohammed
    Adib, Abdellah
    Powiązania:
    https://bibliotekanauki.pl/articles/2141900.pdf
    Data publikacji:
    2020
    Wydawca:
    Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
    Tematy:
    deep learning
    convolutional neural network
    brain computer interface
    P300
    classification
    Opis:
    A Brain‐Computer Interface (BCI) is an instrument capa‐ ble of commanding machine with brain signal. The mul‐ tiple types of signals allow designing many applications like the Oddball Paradigms with P300 signal. We propose an EEG classification system applied to BCI using the con‐ volutional neural network (ConvNet) for P300 problem. The system consists of three stages. The first stage is a Spatiotemporal convolutional layer which is a succession of temporal and spatial convolutions. The second stage contains 5 standard convolutional layers. Finally, a lo‐ gistic regression is applied to classify the input EEG sig‐ nal. The model includes Batch Normalization, Dropout, and Pooling. Also, It uses Exponential Linear Unit (ELU) function and L1‐L2 regularization to improve the lear‐ ning. For experiments, we use the database Dataset II of the BCI Competition III. As a result, we get an F1‐score of 53.26% which is higher than the BN3 model.
    Źródło:
    Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 4; 58-63
    1897-8649
    2080-2145
    Pojawia się w:
    Journal of Automation Mobile Robotics and Intelligent Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Image Recall Using a Large Scale Generalized Brain-state-in-a-box Neural Network
    Autorzy:
    Oh, Ch.
    Żak, S. H.
    Powiązania:
    https://bibliotekanauki.pl/articles/908482.pdf
    Data publikacji:
    2005
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    pamięć asocjacyjna
    sieć neuronowa
    pamięć obrazu
    associative memory
    Brain-State-in-a-Box (BSB) neural network
    overlapping decomposition
    image recall
    Opis:
    An image recall system using a large scale associative memory employing the generalized Brain-State-in-a-Box (gBSB) neural network model is proposed. The gBSB neural network can store binary vectors as stable equilibrium points. This property is used to store images in the gBSB memory. When a noisy image is presented as an input to the gBSB network, the gBSB net processes it to filter out the noise. The overlapping decomposition method is utilized to efficiently process images using their binary representation. Furthermore, the uniform quantization is employed to reduce the size of the data representation of the images. Simulation results for monochrome gray scale and color images are presented. Also, a hybrid gBSB-McCulloch-Pitts neural model is introduced and an image recall system is built around this neural net. Simulation results for this model are presented and compared with the results for the system employing the gBSB neural model.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2005, 15, 1; 99-114
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-5 z 5

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