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  • DAMTRNN: A Delta Attention-Based Multi-task RNN for Intention Recognition

    Author(s)
    Chen, Weitong
    Yue, Lin
    Li, Bohan
    Wang, Can
    Sheng, Quan Z
    Griffith University Author(s)
    Wang, Can
    Year published
    2019
    Metadata
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    Abstract
    Recognizing human intentions from electroencephalographic (EEG) signals is attracting extraordinary attention from the artificial intelligence community because of its promise in providing non-muscular forms of communication and control to those with disabilities. So far, studies have explored correlations between specific segments of an EEG signal and an associated intention. However, there are still challenges to be overcome on the road ahead. Among these, vector representations suffer from the enormous amounts of noise that characterize EEG signals. Identifying the correlations between signals from adjacent sensors on a ...
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    Recognizing human intentions from electroencephalographic (EEG) signals is attracting extraordinary attention from the artificial intelligence community because of its promise in providing non-muscular forms of communication and control to those with disabilities. So far, studies have explored correlations between specific segments of an EEG signal and an associated intention. However, there are still challenges to be overcome on the road ahead. Among these, vector representations suffer from the enormous amounts of noise that characterize EEG signals. Identifying the correlations between signals from adjacent sensors on a headset is still difficult. Further, research not yet reached the point where learning models can accept decomposed EEG signals to capture the unique biological significance of the six established frequency bands. In pursuit of a more effective intention recognition method, we developed DAMTRNN, a delta attention-based multi-task recurrent neural network, for human intention recognition. The framework accepts divided EEG signals as inputs, and each frequency range is modeled separately but concurrently with a series of LSTMs. A delta attention network fuses the spatial and temporal interactions across different tasks into high-impact features, which captures correlations over longer time spans and further improves recognition accuracy. Comparative evaluations between DAMTRNN and 14 state-of-the-art methods and baselines show DAMTRNN with a record-setting performance of 98.87% accuracy.
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    Conference Title
    Lecture Notes in Computer Science
    Volume
    11888
    DOI
    https://doi.org/10.1007/978-3-030-35231-8_27
    Subject
    Information and computing sciences
    Science & Technology
    Computer Science, Artificial Intelligence
    Attention network
    Publication URI
    http://hdl.handle.net/10072/401607
    Collection
    • Conference outputs

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