Analysis of the relationship between EEG signal and aging through Linear Discriminant Analysis (LDA)

this paper aims to establish the correlation between statistical parameters and electroencephalographic (eeg) signals as a function of age, in subjects without neurological disorders. eeg signals were recorded during the task of following an archimedes spiral. there were 59 healthy subjects who voluntarily participated in this study which were divided into 7 groups, aging between 20 to 86 years from both gender, in order to identify differences and allow discrimination between the features of each group. initially, comparisons were made among several features (f20, f50, f80, f95, mean frequency, root mean square value, zero crossings, square of the power spectrum, kurtosis, skewness, variance, standard deviation and approximate entropy) seeking separation between young and elderly groups. furthermore, it was sought to correlate the statistical parameters and the entire age range. for this purpose it was used linear discriminant analysis (lda). the data were processed with matlab？ software. through the lda, significant differences were observed over the distinct age ranges. the tool has satisfactorily performed the separation of discriminant features by classifying groups of subjects in function of their age range.