Translator Disclaimer
15 March 2011 A mean-sensitive spatial filtering (MSF) method for trial-by-trial analysis of N170 component
Author Affiliations +
N170 is an important neurophysiological index to study the underlying mechanisms of face and object perception. In this study, we proposed a mean-sensitive spatial filtering (MSF) method for linear transformation of event-related potentials (ERP) that is sensitive to mean differences between stimuli conditions and applied it to N170 component to extract category-specific spatio-temporal features contained in EEG. MSF method estimated a set of optimal projecting vectors according to the spatial distribution patterns of N170 means. Then, we applied these spatial filters to single-trial ERP data and perform classification on the extracted features. In this way, the presence of a larger negative component in EEG time courses evoked by faces can be detected robustly in single trial EEG, and hereby we can infer the category of every presented stimulus from faces and objects. Furthermore, we also successfully extracted the unobvious distinct spatial patterns between cars and cats with MSF and separated them correctly. Our remarkable and robust classification performances suggest that MSF works well in extracting stable spatial patterns from N170. Therefore, MSF provides a promising solution for decoding presented visual information basing on single-trial N170 component.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Changming Wang, Jiacai Zhang, Li Yao, and Xiaoping Hu "A mean-sensitive spatial filtering (MSF) method for trial-by-trial analysis of N170 component", Proc. SPIE 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging, 79652B (15 March 2011);

Back to Top