Discrimination-target electroencephalographic data input from a discrimination-target electroencephalographic data input portion is converted into feature parameters on a phase space and feature parameters on a frequency space by a feature parameter extracting portion. By use of feature parameters generated likewise from a reference learning electroencephalographic data set input from a reference learning electroencephalographic data set input portion, a reference data space calculating portion calculates a mean, a variance, and an inverse matrix of a correlation matrix of the reference learning electroencephalographic data set. These are used as a reference data space. A Mahalanobis distance calculating portion obtains a Mahalanobis distance from the mean, the variance, and the inverse matrix of the correlation matrix of the reference learning electroencephalographic data set calculated as a reference data space, and the feature parameters calculated from the discrimination-target electroencephalographic data. A judgment portion judges normality/abnormality of the discrimination-target electroencephalogram according to the Mahalanobis distance.
Automatic electroencephalogram analysis apparatus and method