Improving object segmentation by using EEG signals and rapid serial visual presentation
This paper extends our previous work on the potential of EEG-based brain computer interfaces to segment salient objects in images. The proposed system analyzes the Event Related Potentials (ERP) generated by the rapid serial visual presentation of windows on the image. The detection of the P300 sign...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/76579 |
| Acceso en línea: | https://hdl.handle.net/2117/76579 https://dx.doi.org/10.1007/s11042-015-2805-0 |
| Access Level: | acceso abierto |
| Palabra clave: | Digital video Image processing--Digital techniques Computational neuroscience Brain-computer interfases Image segmentation Vídeo digital Imatges -- Processament -- Tècniques digitals Imatges -- Segmentació Ordinadors neuronals Neurociència computacional Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo Àrees temàtiques de la UPC::So, imatge i multimèdia::Creació multimèdia::Vídeo digital |
| Sumario: | This paper extends our previous work on the potential of EEG-based brain computer interfaces to segment salient objects in images. The proposed system analyzes the Event Related Potentials (ERP) generated by the rapid serial visual presentation of windows on the image. The detection of the P300 signal allows estimating a saliency map of the image, which is used to seed a semi-supervised object segmentation algorithm. Thanks to the new contributions presented in this work, the average Jaccard index was improved from 0.47 to 0.66 when processed in our publicly available dataset of images, object masks and captured EEG signals. This work also studies alternative architectures to the original one, the impact of object occupation in each image window, and a more robust evaluation based on statistical analysis and a weighted F-score. |
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