A novel method for estimating functional connectivity from EEG coherence potentials.

Analysis of functional connectivity can provide insights into how the brain performs various cognitive and behavioral tasks as well as the neural mechanisms underlying several pathologies. In this work, we describe a novel approach to estimate functional connectivity from electroencephalography (EEG) data using the concept of coherence potentials (CPs), which are defined as clusters of high-amplitude deflections with similar waveform shapes. We define connectivity measures based on features of CPs, including the time intervals between CP peaks and their co-occurrence on different electrodes or channels. We used EEG data from 25 healthy subjects performing three tasks - resting state (eyes closed and eyes open), working memory and pattern completion tasks to investigate the ability of CP based connectivity measures to distinguish between these tasks. When compared with traditional connectivity measures including several spectral-based measures and mutual information, our results showed that CP based connectivity measures more robustly and significantly distinguished between all the tasks both at group-level and subject-level. In conclusion, CP based EEG connectivity measures provide a reliable way to distinguish between different cognitive task conditions and could pave way in the early detection of neurological disorders such as Alzheimer's disease that affect various cognitive tasks.