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This paper aims to evaluate and compare different spatial filtering methods on EEG data in order to improve the classification of Motor Imagery electrical brain information. Multiple spatial filtering methods, such as Laplacian filter, Common Average Reference method, Weighted Average Filter, Spatial Smoothing Filter, Common Spatial Patterns method and the Non-Homogeneous filter, have been analyzed...
Emotional processing of ex-combatants is affected by chronic exposure to violent events. For a successful reintegration into society, it is necessary to discriminate their brain responses from civilian people, as a first stage to develop treatment strategies. This paper presents a comparative analysis between a Multilayer Perceptron Neural Network and a Fuzzy C-Means classifier to differentiate ex-combatant...
Brain Computer Interfaces allow the interaction between a person and their environment using signals extracted directly from the brain. One of the most common non-invasive methods of brain signal acquisition is the electroencephalography (EEG). An EEG based BCI system requires the processing and translation of the EEG signal into significant features that could be converted into commands for the external...
EEG recordings are often contaminated by environmental noise and physiological factors commonly known as artifacts, which affect the quality of the signal. This paper presents an automatic method to classify between artifactual and neural components in EEG signals using an Independent Component Analysis (ICA) and a Support Vector Machine. With the resultant model, we obtained a classification accuracy...
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