Constructing weighted networks based on EEG data segmentation for brain wave pattern recognition

The human brain is an extremely complex study. In recent years, EEG (Electroencephalography) data sets have been studied extensively in the various fields, particularly in the area of brain study. Some psychological work has suggested that human brains can generate EEG signals that are based on individual entities. These EEG signals varies according to different identities. This paper suggests a weighted network method for brain pattern recognition. The data segmentation technique is deployed in the process of constructing weighted networks. The EEG data segmentation technique incorporates the normal distribution sampling method [1]. The EEG data sets are obtained from various experiments including shopping psychological EEG data sets, driving EEG data sets, etc. This research discovers the potential of generating an efficient network for brain wave pattern recognition. The future work will further extend the current work and applied the proposed method to the human-robotic control and security areas.