Goal: Understanding the dynamics of brain function through non-invasive monitoring techniques requires the development of computational methods that can deal with the non-stationary properties of recorded activities. As a solution to this problem, a new data-driven segmentation method for recordings obtained through electroencephalography (EEG) is presented. Methods: The proposed method utilizes singular value decomposition (SVD) to identify the time intervals in the EEG recordings during which the spatial distribution of clusters of active cortical neurons remains quasi-stationary. Theoretical analysis shows that the spatial locality features of these clusters can be, asymptotically, captured by the most significant left singular subspace of the EEG data. A reference/sliding window approach is employed to dynamically extract this feature subspace, and the running projection error is monitored for significant changes using Kolmogorov-Smirnov test. Results: Simulation results, for a wide range of possible scenarios regarding the spatial distribution of active cortical neurons, show that the algorithm is successful in accurately detecting the segmental structure of the simulated EEG data. The algorithm is also applied to experimental EEG recordings of a modified visual oddball task. Results identify a unique sequence of dynamic patterns in the event-related potential (ERP) response to each of the three involved stimuli. Conclusion: The proposed method, without using source localization methods or scalp topographical maps, is able to identify intervals of quasi-stationarity in the EEG recordings. Significance: The proposed segmentation technique can offer new insights on the dynamics of functional organization of the brain in action.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Electroencephalography (EEG)
- dynamics of brain function
- singular value decomposition (SVD)