Diagnosis Autism by Fisher Linear Discriminant Analysis FLDA via EEG

Diagnosis of autism is one of the difficult problems facing researchers. In this paper, Electroencephalogram (EEG) based Autism diagnosis using Fisher Linear Discriminat (FLD) Analysis is presented. Multivariate analyses of all the channels (via the concatenated signals) were used. Different preprocessing techniques, different ensemble averages, as well as, different feature extraction techniques are studied. The average correct rates are (90%). Raw data features and FFT features are used. Windsor Filtered Data gave the best mean and the lower standard deviation of both raw and FFT features. Over all, FFT features have a better correct rate of 88.14% and lower standard deviation 0.0404 than raw features.