An adaptive optimal-Kernel time-frequency representation-based complex network method for characterizing fatigued behavior using the SSVEP-based BCI system

The Steady State Visual Evoked Potential (SSVEP)-based Brain Computer Interface (BCI) system has seen extensively applications in many fields, such as physical recovery of handicap persons, obstacle avoidance of intelligent vehicles, entertainment and smart homes. However, subjects easily get fatigued because of the involving long-time operations. The presence of fatigue symptoms typically affect the efficiency of the BCI system, so investigating the effects of fatigue on the SSVEP classification accuracy from the perspective of brain network becomes a challenging issue of significant importance. In this paper, we develop an adaptive optimal-Kernel time-frequency representation (AOK-TFR)-based complex network method for characterizing fatigued behavior using the SSVEP-based BCI system. We apply the traditional Canonical Correlation Analysis (CCA) and Fisher Linear Discriminant Analysis (FLDA) to classify SSVEP signals. We find that the classification accuracy at the fatigue states is significantly lower than that at the normal states. To reveal the reasons, we infer and analyze the AOK-TFR-based functional brain network with SSVEP signals. In particular, we calculate the AOK-TFR of the acquired 30-channel SSVEP signals under both normal and fatigue conditions and then construct a brain network in terms of the two-norm distance between different channels. Our results suggest that the small-world-ness of the network at normal states is prominent, and the main brain regions associated with SSVEP are in the prefrontal cortex and occipital lobe. Our analysis sheds new insights into the understanding and management of the fatigued behavior using the SSVEP-based BCI system.