A different perspective of multi-channel EEG data using network analysis

The paper presents a different perspective of multi channel electroencephalograph (EEG) data based on network analysis. Three statistical parameters are introduced here: node strength; average path length; and clustering coefficient. They measure the strength, integration and segregation characteristics of the functional EEG network. These parameters provide valuable insights into the brain network of the subject. It is easily evaluated as illustrated here using a thirty electrode EEG data. Results from this study show the EEG network exhibits a high degree of strength, integration and segregation compared to, when all the recorded signals in the network are only noise.The vulnerability of the network tested by replacing one at time the brain signals in the electrodes with noise, showed segregation to be the least vulnerable. Loss in segregation in the network was not observed when a single good electrode is replaced by a noisy one, but only when a number of them were replaced.

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