16 March 2011 A mean-sensitive spatial filtering (MSF) method for trial-by-trial analysis of N170 component
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Proceedings Volume 7965, Medical Imaging 2011: Biomedical Applications in Molecular, Structural, and Functional Imaging; 79652B (2011); doi: 10.1117/12.877544
Event: SPIE Medical Imaging, 2011, Lake Buena Vista (Orlando), Florida, United States
Abstract
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.
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Changming Wang, Jiacai Zhang, Li Yao, 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 (16 March 2011); doi: 10.1117/12.877544; https://doi.org/10.1117/12.877544
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KEYWORDS
Spatial filters

Electroencephalography

Visualization

Feature extraction

Image classification

Brain

Facial recognition systems

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