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. 2020 Jun 15:2020:4930972.
doi: 10.1155/2020/4930972. eCollection 2020.

Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface

Shi Qiu  1 , Junjun Li  2 , Mengdi Cong  3 , Chun Wu  4 , Yan Qin  4 , Ting Liang  2   5
Affiliations

Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface

Shi Qiu et al. Comput Math Methods Med. .

Abstract

Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on a brain-computer interface. First, a single channel time-frequency feature extraction model is constructed based on the analysis of EEG data. Second, a multilayer fusion model is proposed to establish the brain-computer interface by connecting the brain electrical signal with a computer. Finally, according to image presentation, a three-frame image presentation method with different window widths and window positions is proposed to effectively detect the solitary pulmonary nodules.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
The algorithm flow chart.
Figure 2
Figure 2
Deep learning network based on multiple features.
Figure 3
Figure 3
Experiment data presentation.
Figure 4
Figure 4
Recognition ROC figure.
Figure 5
Figure 5
Brain response mapping.
Figure 6
Figure 6
Time-recognition accuracy graph.
Figure 7
Figure 7
Ratio-recognition accuracy graph.
Figure 8
Figure 8
Presentation time-recognition accuracy relationship graph.
Figure 9
Figure 9
ROC curves of different methods.
Figure 10
Figure 10
ROC curves of different algorithms.
See this image and copyright information in PMC

References

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