An EEG Based Predictive Model for Fibromyalgia

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Project Lifespan: 2013 - 2016

The main goal of the project was to identify the underlying causes of fibromyalgia under the evoked related potential paradigm using advanced signal processing techniques and machine learning algorithms. Patients were given precise weighted stimulations and their EEG’s were recorded. Various averaging and feature extraction methods were applied such as magnitude and phase coherence analysis to identify event-related desynchronization (ERD) or event-related synchronization (ERS). EEG source localization methods were also used to identify spatial information for anatomical analysis. Neural network and support vector clustering was performed to identify clustering as well as discrimination between patient and normal control group. For more information please go to Tactile Health Website.