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- W4226530487 abstract "Sustained attention is a cognitive ability that allows us to maintain our concentration for long periods of time. Every day, we engage in a variety of activities that necessitate our full attention. While performing these tasks, it is critical to ignore the various types of distractions in our surroundings. This study focuses on the spectral changes in electroencephalography (EEG) during a driving simulation task in the presence of disturbances in the form of visual stimuli at different intervals. Significant changes in brain oscillation patterns were discovered when their EEG spectra were evaluated based on response times, which indicated their level of visual alertness to randomly introduced visual stimuli. Changes in alpha and theta EEG powers over the frontal and parietal regions of the brain preceded response time prolongation across subjects and sessions. The proposed work in this paper presents thorough research into the identification and enhancement of sustained attention for an individual with the help of machine learning and deep learning models. The recommended model can effectively classify subjects as highly attentive or low attentive in real-time. The system's reliability is computed using learning and ROC curves. The driving simulation dataset included 27 subjects and 64 trials in total, with an average classification accuracy of 82.6 percent and the best performance of 84.20 percent." @default.
- W4226530487 created "2022-05-05" @default.
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- W4226530487 date "2021-12-01" @default.
- W4226530487 modified "2023-09-30" @default.
- W4226530487 title "A Deep Learning Approach to Detect Sustained Attention in Real-Time Using EEG Signals" @default.
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- W4226530487 doi "https://doi.org/10.1109/compe53109.2021.9752135" @default.
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