Analysis and Classification of Evoked Potentials in Response to Familiar and Unfamiliar Faces

Brain activity during perception and recognition of faces have been studied by researchers with the purpose to develop brain-computer interfaces and to study neurological disorders. In this paper, we analyzed evoked potentials as neurophysiological indicators and developed a model based on signal processing and machine learning techniques to find descriptive patterns that allow the differentiation of familiar and unfamiliar faces. We considered wave components such as P1, N170, N250, P300, and N400 to describe the events. Morphological analysis and wavelet transform were used for the feature extraction stage, and support vector machines and binomial logistic regression were evaluated for the classification stage. The best classification results were obtained with the morphological characteristics, where the highest classification accuracy was 80% on average. © 2018 IEEE.

• Fecha de publicación: 2018
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/9204
• Acceso en línea: https://hdl.handle.net/20.500.12585/9204
• Palabra clave: Electroencephalography; Evoked potentials; Face recognition; Machine learning; Wavelet transform; Artificial intelligence; Bioelectric potentials; Biomedical signal processing; Brain; Brain computer interface; Electroencephalography; Electrophysiology; Learning systems; Neurophysiology; Wavelet transforms; Binomial logistic regressions; Classification accuracy; Classification results; Feature extraction stages; Machine learning techniques; Morphological analysis; Morphological characteristic; Perception and recognition; Face recognition
• Rights: restrictedAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/