The aim of this study is to investigate the search time regulation of objectives and eye movement behavior characteristics in the multi-objective visual search. The experimental task was accomplished with computer programming and presented characters on a 24 inch computer display. The subjects were asked to search three targets among the characters. Three target characters in the same group were of high similarity degree while those in different groups of target characters and distraction characters were in different similarity degrees. We recorded the search time and eye movement data through the whole experiment. It could be seen from the eye movement data that the quantity of fixation points was large when the target characters and distraction characters were similar. There were three kinds of visual search patterns for the subjects including parallel search, serial search, and parallel-serial search. In addition, the last pattern had the best search performance among the three search patterns, that is, the subjects who used parallel-serial search pattern spent shorter time finding the target. The order that the targets presented were able to affect the search performance significantly; and the similarity degree between target characters and distraction characters could also affect the search performance.
As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.