The effect of time window length on dynamic brain network analysis under various emotional conditions

Abstract

Emotion affects human being's health to a great extent and it attracts lots of attention recently. Objective measurements are necessary for identifying various emotion states. As a powerful technique on high temporal resolution, Electroencephalogram (EEG) provides an effective way to quantify emotion impersonally, especially uncovering the dynamic characteristics. The implementation of time window length is one popular method for extracting dynamic interactions in the brain network. In this paper, the effect of time window length is revealed on dynamic brain network investigation. A quantitative pipeline is proposed based on brain network extraction and community mining in this paper. The EEG data of healthy subjects are recorded under sadness, happiness and neutral emotions that induced through video stimulation. Since beta (13-30Hz) band EEG signals are believed to play important role in cognitive processing, this paper focuses on the beta band EEG signals. Phase locking value (PLV) was adopted to calculate the functional interactions among brain areas. A nonoverlapping time window was taken in to produce dynamic connections. As a comparison, the dynamic brain networks were extracted by the length of 2s and 5S sliding time windows respectively. Later, the Louvain algorithm was used to detect the communities. As the member of community changes over time, the stationarity is quantified to measure the evolution of the varying communities in the brain network. The preliminary results illustrated that significant difference exists among communities from the beta band EEG networks that derived from 2s and 5s time windows separately. It is suggested that the choice of sliding time window will affect the stationarity quantification of the dynamic evolution of the brain network under various emotional conditions. It is necessary to pay attention to the choice of time length window when uncovering dynamic brain network changes in the future.