The invention discloses an
analysis method and application of EEG signals based on a
complex network. The
analysis method of the EEG signals based on the
complex network comprises the following steps: constructing multi-scale level limited penetrable
visibility graph complex networks; calculating characteristic indexes of each multi-scale level limited penetrable
visibility graph
complex network; combining a
support vector machine to classify the EEG signals, namely using a leave-one-out cross-validation and
support vector machine classifier to classify all two-dimensional index vectors, and using a ten-fold cross-validation and
support vector machine classifier to classify all the two-dimensional index vectors. According to the invention, multi-scale ideas and level limited penetrable
visibility graph theories are combined to construct an EEG multi-scale level limited penetrable
visibility graph complex network so as to extract complex network indexes, and the
support vector machine classifier in
machine learning is combined to realize high-accuracy classification for different EEG signals. The
analysis method and application of the EEG signals based on the complex network can be applied to smart head-mounted wearable equipment, and sleep EEG signals are measured through analyzing the smart wearable equipment to monitor the
brain state of a user, furthermore, necessary early warning can be provided.