Human implicit intent recognition based on the phase synchrony of EEG signals

This paper proposes a human implicit intent recognition system based on electroencephalography (EEG) signals, for developing an advanced interactive web service engine. We focus on identifying brain state transitions between intentions, and classifying a user's implicit intentions while viewing an image on a web page, based on an EEG experiment. We measure brain state changes between a navigational intention and an informational intention by using phase synchrony; i.e., the phase locking value (PLV) in an EEG. Comparing PLVs that correspond to the two intention states is useful for determining a human's implicit intention. In order to discriminate between a user's implicit intentions using a PLV, we must extract features based on an EEG analysis. For this purpose, we identify the most reactive band within the full band of brain signals. Theta (4-7 Hz), alpha (8-13 Hz), beta-1 (14-22 Hz), and beta-2 (23-30 Hz) bands are used to extract the EEG features from the most reactive EEG band. Subsequently, we select the most significant pairs (MSPs) that are highly reactive and correspond to the intention. According to the proposed method, these features are useful for: (i) showing the brain state transitions regarding intentions, and (ii) classifying a human's implicit intention using several classifiers such as a support vector machine (SVM), Gaussian Mixture Model (GMM), and Naïve Bayes. We then compare the results of these classifiers. This study demonstrates the potential uses of these identified brain electrode pairs for cognitive detection and task classification in future brain-computer interface (BCI) applications.