Schizophrenia Classification using Resting State EEG Functional Connectivity: Source Level Outperforms Sensor Level
Disrupted Functional and Structural Connectivity Measures Have Been Used to Distinguish Schizophrenia Patients from Healthy Controls. Classification Methods based on Functional Connectivity Derived from EEG Signals Are Limited by the Volume Conduction Problem. Recorded Time Series at Scalp Electrodes Capture a Mixture of Common Sources Signals, Resulting in Spurious Connections. We Have Transformed Sensor Level Resting State EEG Times Series to Source Level EEG Signals Utilizing a Source Reconstruction Method. Functional Connectivity Networks Were Calculated by Computing Phase Lag Values between Brain Regions at Both the Sensor and Source Level. Brain Complex Network Analysis Was Used to Extract Features and the Best Features Were Selected by a Feature Selection Method. a Logistic Regression Classifier Was Used to Distinguish Schizophrenia Patients from Healthy Controls at Five Different Frequency Bands. the Best Classifier Performance Was based on Connectivity Measures Derived from the Source Space and the Theta Band.The Transformation of Scalp EEG Signals to Source Signals Combined with Functional Connectivity Analysis May Provide Superior Features for Machine Learning Applications.
S. Azizi et al., "Schizophrenia Classification using Resting State EEG Functional Connectivity: Source Level Outperforms Sensor Level," Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 1770 - 1773, Institute of Electrical and Electronics Engineers, Jan 2021.
The definitive version is available at https://doi.org/10.1109/EMBC46164.2021.9630713
Electrical and Computer Engineering
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01 Jan 2021
Army Research Laboratory, Grant W911NF-14-2-0034