Abstract: The current innovation pertains to the medical data processing domain and introduces a method for formulating an iNPH prediction model using a new/old stimulus BCI paradigm. The process involves conducting new/old stimulus BCI paradigm experiments on a target population both before and after the Lumbar Tap Test, obtaining electroencephalogram (EEG) signal data. After preprocessing the EEG signal data, the model extracts features related to new and old stimulus event-related potentials before and after the test, specifically focusing on P600 amplitude features. The iNPH prediction model is then trained based on these event-related potential features. This innovation facilitates quantifying cognitive function improvement, aiding physicians in prompt iNPH diagnosis and enabling timely and effective patient treatment.

Abstract: The present invention relates to the technical field of medical data processing, and discloses an iNPH prediction method based on a visual Oddball paradigm, comprising: performing the same visual Oddball paradigm experiments on a target population before and after the target population undergoes LTT, and obtaining an electroencephalography (EEG) signal data; Pre-processing the EEG signal data, and obtaining an event-related potential feature, the event-related potential feature being a P300 amplitude feature; Based on the event-related potential feature, the iNPH prediction model is trained to obtain the iNPH prediction model.

Abstract: The disclosure belongs to the technical field related to additive manufacturing model preprocessing, and discloses a field-based additive manufacturing digital model feature identification and extraction method and device, the method can convert the digital model represented by the facet into a signed distance field and introduce a simulated physical field of the forming/service simulation, the feature distance field after the frequency domain analysis filtering, and the geometric feature field obtained by the multi-precision convolution unit analysis according to the requirement of feature to be identified, then, multiple fields are combined to realize feature classification determination and labeling of features, and finally feature extraction is completed based on field data and isosurface and isoline reconstruction algorithms.

Abstract: Disclosed are free-form curved surface slicing method and device based on implicit model, includes following: (1) Using mesh model of layered curved surface to intersect with implicit model of a part to be manufactured to obtain contour lines; (2) Comparing rules for intersecting contour lines and meshes within mesh, mesh coordinates are substituted into implicit model, obtaining a position of an intersection point of contour lines in corresponding mesh; (3) Using interpolation method to calculate intersection coordinates of contour lines and mesh boundary, specifying storing sequence of interpolation points, storing intersection points of discrete line segments in mesh; (4) Merging unordered point sets obtained to establish topological relationship between point sets and discrete line segments, each line segment corresponds to two endpoints, each endpoint corresponds to two line segments, based on above, all points traversed will be connected into complete and ordered profile, that is sliced profile.