Sleep scoring is manually marked by professional neurophysicians, which is a time-consuming and subjective error prone work. An automatic classification algorithm dealing with this task is of great significance to auxiliary medical diagnose. In this paper, data set are collected from the clinical physiological signals of 7 patients from Xianga Hospital. Signal preprocessing and feature extraction algorithm combines the advantages of principal component analysis and wavelet decomposition in only single EEG channel. In result, the five fold cross validation on the data set of all 7 patients has achieved an average accuracy of 77.18% and the highest accuracy of 83.24%. In addition, it is also found that the stable and optimal EEG channel is C3-M2, and the extracted 41 wavelet statistical features are not sensitive to signal noise, and it is found that the random forest model has better classification performance.
With the development of quad-rotor UAVs, UAVs have been widely used in civilian fields such as aerial photography, agricultural production, surveillance and rescue. Controlling drones is a complex and difficult task for human operators. Therefore, it is of great significance to reduce the burden of human operation and improve the efficiency of drones when performing tasks. With the development of some novel human-computer interaction technologies and the continuous improvement of drone autonomy, manipulating drones can become easier and more interesting. In this work, we designed a method that uses operator gaze to assist drone control and target following. We conducted verification experiments on the Tello UAV, and the experimental results show that eye tracking interaction makes the human operation process more natural, and the combination of eye movement behavior and semi-autonomous drone control further reduces the burden on operators and improves the reliability of drone missions.
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