Electroencephalograph Automatic Diagnosis Based on Kernel Principal Angle

Electroencephalograph (EEG) is the most important method in diagnosis of brain diseases. Unfortunately, the doctor interprets EEG according to personal experiences, which results in over interpretation or incorrect judgment. In this paper, we derive a sparse kernel principal angle (SKPA). The SKPA is applied to disease diagnosis in examples: we select Electroencephalograph data of different diseases, calculate the principal angles between the datasets. Reading the principal angles, one can find diseases or even diagnose disease type. The electroencephalograph automatic diagnosis system based on kernel principal angle will be helpful to doctors.

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