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    Emotion Recognition Based on Fusion of Local Cortical Activations and Dynamic Functional Networks Connectivity: An EEG Study

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    Date
    2019
    Author
    Yahya, Fares
    Tariq, Usman
    Alex, Meera
    Mir, Hasan
    Al-Nashash, Hasan
    Advisor(s)
    Unknown advisor
    Type
    Peer-Reviewed
    Article
    Published version
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    Abstract
    In this paper, we present a method to improve emotion recognition based on the fusion of local cortical activations and dynamic functional network patterns. We estimate the cortical activations using power spectral density (PSD) with the Burg autoregressive model. On the other hand, we estimate the functional connectivity networks by utilizing the phase locking value (PLV). The results of cortical activations and connectivity networks show different patterns across three emotions at all frequency bands. Similarly, the results of fusion significantly improve the classification rate in terms of accuracy, sensitivity, specificity and the area under the receiver operator characteristics curve (AROC), p < 0:05. The average improvement with fusion in all evaluation metrics are 6.84% and 4.1% when compared to PSD and PLV alone, respectively. The results clearly demonstrate the advantage of fusion of cortical activations with dynamic functional networks for developing human-computer interaction system in real-world applications.
    DSpace URI
    http://hdl.handle.net/11073/16599
    External URI
    https://doi.org/10.1109/ACCESS.2019.2944008
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    • Biosciences and Bioengineering
    • Department of Electrical Engineering

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