Abstract
Monitoring work performance declined has become need solution of important security problem, to prevent operator monitoring performance decline. This research based on simulated monitoring task collected 16 scalp sites EEG signal, by bands filter and wavelet analysis. We found that, in the site of central lobe, parietal lobe, occipital lobe, alpha wave advantage activities (cosine similarity of alpha wave and original waveform) associated with monitoring performance (hit rate). Alpha wave activity increases leading to reduced monitor performance. Alpha wave activity increases as monitor’s performance decline. While uses the curve-estimate for the regression analysis we found that quadratic function and cubic function more than the linear function could reflect the relevance of the advantage of alpha wave activity and monitor performance. On the right brain scalp occipital site (O2), the quadratic function model reflects best correlation between the advantage of alpha waves activities and the monitoring performance, and it will fit the helmet acquisition of EEG electrodes placement in actual vigilance work.
Project supported by National Natural Science Foundation of china (No. 31260238).
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Acknowledgements
We thank the Students and control subjects for their participation. The skillful work by EEG technician Mr. Deqian Zhang gratefully acknowledged. This study was financed from the National Natural Science Foundation of China (No. 31260238), the regional agreement on medical training and clinical research in Jinggangshan University, Jian City project.
Compliance with Ethical Standards
The study was approved by the Logistics Department for Civilian Ethics Committee of the Jinggangshan University.
All subjects who participated in the experiment provided signed informed consent form.
All relevant ethical safeguards have been met with regard to subject protection.
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Zhang, D., Cheng, W., Yang, H. (2018). Predict the Performance of Visual Surveillance by EEG Spectral Band Advantage Activity: Modeling-Based Occipital Alpha Waves Advantage Activity. In: Long, S., Dhillon, B. (eds) Man–Machine–Environment System Engineering. MMESE 2017. Lecture Notes in Electrical Engineering, vol 456. Springer, Singapore. https://doi.org/10.1007/978-981-10-6232-2_26
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DOI: https://doi.org/10.1007/978-981-10-6232-2_26
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