  Objective. The paper investigates the presence of autism using the functional
brain connectivity measures derived from electro-encephalogram (EEG) of
children during face perception tasks. Approach. Phase synchronized patterns
from 128-channel EEG signals are obtained for typical children and children
with autism spectrum disorder (ASD). The phase synchronized states or
synchrostates temporally switch amongst themselves as an underlying process for
the completion of a particular cognitive task. We used 12 subjects in each
group (ASD and typical) for analyzing their EEG while processing fearful, happy
and neutral faces. The minimal and maximally occurring synchrostates for each
subject are chosen for extraction of brain connectivity features, which are
used for classification between these two groups of subjects. Among different
supervised learning techniques, we here explored the discriminant analysis and
support vector machine both with polynomial kernels for the classification
task. Main results. The leave one out cross-validation of the classification
algorithm gives 94.7% accuracy as the best performance with corresponding
sensitivity and specificity values as 85.7% and 100% respectively.
Significance. The proposed method gives high classification accuracies and
outperforms other contemporary research results. The effectiveness of the
proposed method for classification of autistic and typical children suggests
the possibility of using it on a larger population to validate it for clinical
practice.
