Temat: brain network - Katalog OPAC zbiorów

Skocz do pozycji: 1.

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

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Skocz do pozycji: 2.

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

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Skocz do pozycji: 3.

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

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Skocz do pozycji: 4.

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

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