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  1. Article ; Online: Corrigendum to "Functional Connectivity Learning via Siamese-based SPD Matrix Representation of Brain Imaging Data" [Neural Networks 163 (2023) 272-285].

    Tang, Yunbo / Chen, Dan / Wu, Jia / Tu, Weiping / Monaghan, Jessica J M / Sowman, Paul / Mcalpine, David

    Neural networks : the official journal of the International Neural Network Society

    2023  Volume 164, Page(s) 575

    Language English
    Publishing date 2023-05-23
    Publishing country United States
    Document type Published Erratum
    ZDB-ID 740542-x
    ISSN 1879-2782 ; 0893-6080
    ISSN (online) 1879-2782
    ISSN 0893-6080
    DOI 10.1016/j.neunet.2023.05.012
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2365: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: Functional connectivity learning via Siamese-based SPD matrix representation of brain imaging data.

    Tang, Yunbo / Chen, Dan / Wu, Jia / Tu, Weiping / Monaghan, Jessica J M / Sowman, Paul / Mcalpine, David

    Neural networks : the official journal of the International Neural Network Society

    2023  Volume 163, Page(s) 272–285

    Abstract: Measurement of brain functional connectivity has become a dominant approach to explore the interaction dynamics between brain regions of subjects under examination. Conventional functional connectivity measures largely originate from deterministic models ...

    Abstract Measurement of brain functional connectivity has become a dominant approach to explore the interaction dynamics between brain regions of subjects under examination. Conventional functional connectivity measures largely originate from deterministic models on empirical analysis, usually demanding application-specific settings (e.g., Pearson's Correlation and Mutual Information). To bridge the technical gap, this study proposes a Siamese-based Symmetric Positive Definite (SPD) Matrix Representation framework (SiameseSPD-MR) to derive the functional connectivity of brain imaging data (BID) such as Electroencephalography (EEG), thus the alternative application-independent measure (in the form of SPD matrix) can be automatically learnt: (1) SiameseSPD-MR first exploits graph convolution to extract the representative features of BID with the adjacency matrix computed considering the anatomical structure; (2) Adaptive Gaussian kernel function then applies to obtain the functional connectivity representations from the deep features followed by SPD matrix transformation to address the intrinsic functional characteristics; and (3) Two-branch (Siamese) networks are combined via an element-wise product followed by a dense layer to derive the similarity between the pairwise inputs. Experimental results on two EEG datasets (autism spectrum disorder, emotion) indicate that (1) SiameseSPD-MR can capture more significant differences in functional connectivity between neural states than the state-of-the-art counterparts do, and these findings properly highlight the typical EEG characteristics of ASD subjects, and (2) the obtained functional connectivity representations conforming to the proposed measure can act as meaningful markers for brain network analysis and ASD discrimination.
    MeSH term(s) Humans ; Autism Spectrum Disorder ; Brain/diagnostic imaging ; Brain Mapping/methods ; Learning ; Magnetic Resonance Imaging/methods
    Language English
    Publishing date 2023-04-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 740542-x
    ISSN 1879-2782 ; 0893-6080
    ISSN (online) 1879-2782
    ISSN 0893-6080
    DOI 10.1016/j.neunet.2023.04.004
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2365: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Book ; Online: A Survey on Deep Learning based Brain Computer Interface

    Zhang, Xiang / Yao, Lina / Wang, Xianzhi / Monaghan, Jessica / Mcalpine, David / Zhang, Yu

    Recent Advances and New Frontiers

    2019  

    Abstract: Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has lifted the performance of brain-computer interface ... ...

    Abstract Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has lifted the performance of brain-computer interface systems significantly in recent years. In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis. We then present a comprehensive survey of deep learning techniques used for BCI, by summarizing over 230 contributions most published in the past five years. Finally, we discuss the applied areas, opening challenges, and future directions for deep learning-based BCI.

    Comment: summarized more than 230 papers most published in the last five years
    Keywords Computer Science - Human-Computer Interaction ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Signal Processing ; Quantitative Biology - Neurons and Cognition
    Subject code 004
    Publishing date 2019-05-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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