Hello, and welcome to our Electrical and Systems Engineering design project at Washington University in St. Louis! We are:

Bridget Ahad, BS in Electrical Engineering, 2017

Ben Bishop, BS in Systems Engineering, 2017

James Wall, BS in Electrical Engineering, 2017

and this is our senior design project from the spring 2017 semester. We’d like to thank our advisors, Dr. ShiNung Ching and Dr. Sina Khanmohammadi, as well as the senior design advisor, Dr. Jason Trobaugh.


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The clinical usefulness of EEG data relies on the identification of biomarkers of disease as a potential diagnostic tool. To explore the potential of using EEG data as a diagnostic tool, two methods of EEG data analysis were proposed by this project: network analysis and time-series analysis. First, network analysis was conducted to compare the spatial interactions in the brain among a control group and test group which contained subjects in a depressed state of consciousness (DLOC). The brain was modeled using an unweighted, undirected graph with nodes corresponding to EEG electrode positions and binary edges determined by calculating the cross-correlation of signals collected from each pair of electrodes and comparing this value to a threshold. The use of multiple thresholds provided an additional dimension with which to compare network interactions since the graphs were constrained to be binary and unweighted for simplicity. In the context of network analysis, potential classification features were identified by calculating network metrics after generating graphs at various threshold values. The key features that were investigated in network analysis were the percentage and frequency of hubs and islands, average number of edges per node, characteristic path length, and average clustering coefficient. Comparisons were drawn between the control and test sets using a combination of these network statistics and spatial connectivity patterns. The calculated network metrics were assessed in the context of the spatial observations made using head-maps generated for each subject and threshold value. In the second part of this project, different time-series techniques used in current EEG research were explored, namely, threshold analysis, autocorrelation analysis, wavelet analysis. After processing the data using these techniques, observations were drawn regarding the distinctions highlighted between the control and experimental groups. Identifying motifs or patterns in the time-evolution of EEG signals unique to a specific patient population can serve as potential biomarkers of disease.