Pani, S. M. | Search bioRxiv

bioRxiv2019

Subject, session and task effects on power, connectivity and network centrality: a source-based EEG study

Inter-subjects variability in functional brain networks has been extensively investigated in the last few years. In this context, unveiling subject-specific characteristics of EEG features may play an important role for both clinical (e.g., biomarkers) and bio-engineering purposes (e.g., biometric systems and brain computer interfaces). Nevertheless, the effects induced by multi-sessions and task-switching are not completely understood and considered. In this work, we aimed to investigate how the variability due to subject, session and task affects EEG power, connectivity and network features estimated using source-reconstructed EEG time-series. Our results point out a remarkable ability to identify subject-specific EEG traits within a given task together with striking independence from the session. The results also show a relevant effect of task-switching, which is comparable to individual variability. This study suggests that power and connectivity EEG features may be adequate to detect stable (over-time) individual properties within predefined and controlled tasks.

bioengineering↗

bioRxiv2019

A comparison between power spectral density and network metrics: an EEG study

Power spectral density (PSD) and network analysis performed on functional correlation (FC) patterns represent two common approaches used to characterize Electroencephalographic (EEG) data. Despite the two approaches are widely used, their possible association may need more attention. To investigate this question, we performed a comparison between PSD and some widely used nodal network metrics (namely strength, clustering coefficient and betweenness centrality), using two different publicly available resting-state EEG datasets, both at scalp and source levels, employing four different FC methods (PLV, PLI, AEC and AECC). Here we show that the two approaches may provide similar information and that their correlation depends on the method used to estimate FC. In particular, our results show a strong correlation between PSD and nodal network metrics derived from FC methods (PLV and AEC) that do not limit the effects of volume conduction/signal leakage. The correlations are less relevant for more conservative FC methods (AECC). These findings suggest that the results derived from the two different approaches may be not independent and should not be treated as distinct analyses. We conclude that it may represent good practice to report the findings from the two approaches in conjunction to have a more comprehensive view of the results.

bioengineering↗