Analysis of eyewitness testimony using electroencephalogram signals

• Autor(a) principal: Mendes, Bruno Miguel Vilela
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/31348
• topic: Eyewitness; Face recognition; BCI; EEG; Feature extraction; Supervised machine learning; SVM; SVM-RFE; ANOVA

The application of Brain Computer Interfaces techniques to vital crime witnesses could and probably will be a key feature in the justice system. Features from the electroencephalogram signals were extracted with information detailing their domain (time or frequency), and their spacial scalp and time placement. For both domains, two different classification pipelines were applied in order to select the most relevant features: one to rank and select the top features and another to recursively eliminate the least relevant feature. The Support Vector Machine (linear and non-linear) is the classification model included in the pipeline. Further observations on the selected features by the applied techniques were performed and discussed in relation to the available knowledge about face recognition. The present work provides an experimental study on the electroencephalogram signals acquired from an experiment in which an array of subjects were asked to identify both culprit and distractor being the culprit related to a previously shown crime scene video.