The study of brain information flow is of great significance to understand brain function in the field of neuroscience. The Granger causality is widely used functional connectivity analysis using multivariate autoregressive model based on the predicted mechanism. High resolution electroencephalogram (EEG) signals of ten healthy subjects were collected with a visual selective attention task. Firstly, independent component analysis was used to extract three spatially independent components of the occipital, parietal, and frontal cortices. Secondly, the Granger causal connectivity was computed between these three regions based on the Granger causality method and then independent sample t-test and bootstrap were used to test the significance of connections. The results showed that Granger causal connectivity existed from frontal to occipital and from parietal to occipital in attentional condition, while causal connectivity from frontal to occipital disappeared in unattentional condition.
Attention can concentrate our mental resources on processing certain interesting objects, which is an important mental behavior and cognitive process. Recognizing attentional states have great significance in improving human’s performance and reducing errors. However, it still lacks a direct and standardized way to monitor a person’s attentional states. Based on the fact that visual attention can modulate the steady-state visual evoked potential (SSVEP), we designed a go/no-go experimental paradigm with 10 Hz steady state visual stimulation in background to investigate the separability of SSVEP features modulated by different visual attentional states. The experiment recorded the EEG signals of 15 postgraduate volunteers under high and low visual attentional states. High and low visual attentional states are determined by behavioral responses. We analyzed the differences of SSVEP signals between the high and low attentional levels, and applied classification algorithms to recognize such differences. Results showed that the discriminant canonical pattern matching (DCPM) algorithm performed better compared with the linear discrimination analysis (LDA) algorithm and the canonical correlation analysis (CCA) algorithm, which achieved up to 76% in accuracy. Our results show that the SSVEP features modulated by different visual attentional states are separable, which provides a new way to monitor visual attentional states.