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- W2895858903 abstract "This paper proposes applying recurrent residual network (RRN) for analyzing electroencephalogram (EEG) data captured during a simulated sustained attention driving task. We first address the suitableness of utilizing residual structure as well as adopting recurrent structure for EEG signal processing. Then based on these descriptions a recurrent residual network is tailored and depicted in detail. Thirdly we use an EEG dataset obtained from a sustained-attention experiment for our model justification. By applying the RRN model to the experimental data and via the competitive result achieved, we demonstrate the elegance of the proposed model. At last, we discuss the characteristics of the learned filters and their interpretations from EEG frequency band perspectives." @default.
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- W2895858903 date "2018-07-01" @default.
- W2895858903 modified "2023-10-09" @default.
- W2895858903 title "Sustained Attention Driving Task Analysis based on Recurrent Residual Neural Network using EEG Data" @default.
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- W2895858903 doi "https://doi.org/10.1109/fuzz-ieee.2018.8491646" @default.
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