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.